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Deep learning of invariant spatio-temporal features from video Chen, Bo 2010

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Deep Learning of Invariant Spatio-Temporal Features from Video by Bo Chen B Sc. Computing Science, Simon Fraser University, 2008 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in THE FACULTY OF GRADUATE STUDIES (Computer Science) The University of British Columbia (Vancouver) August 2010 c© Bo Chen, 2010 Abstract We present a novel hierarchical and distributed model for learning invariant spatio- temporal features from video. Our approach builds on previous deep learning methods and uses the Convolutional Restricted Boltzmann machine (CRBM) as a building block. Our model, called the Space-Time Deep Belief Network (ST- DBN), aggregates over both space and time in an alternating way so that higher layers capture more distant events in space and time. The model is learned in an unsupervised manner. The experiments show that it has good invariance proper- ties, that it is well-suited for recognition tasks, and that it has reasonable generative properties that enable it to denoise video and produce spatio-temporal predictions. ii Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Principles and Models of the Human Visual System . . . . . . . . . 3 2.1 Principles of the Vision Cortex . . . . . . . . . . . . . . . . . . . 3 2.1.1 Hierarchical Representations . . . . . . . . . . . . . . . . 3 2.1.2 Invariance . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.3 Sparseness . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Models Inspired by the Visual Cortex . . . . . . . . . . . . . . . 5 2.2.1 The Simple-Complex Cell Model . . . . . . . . . . . . . 6 2.2.2 Sparse Coding . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.3 Feedforward Neural Networks . . . . . . . . . . . . . . . 7 2.2.4 Undirected Neural Networks . . . . . . . . . . . . . . . . 8 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 Feature Extraction from Video . . . . . . . . . . . . . . . . . . . . . 11 iii 4 Convolutional Restricted Boltzmann Machines . . . . . . . . . . . . 13 4.1 Restricted Boltzmann Machines . . . . . . . . . . . . . . . . . . 13 4.1.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.1.2 Inference and Learning . . . . . . . . . . . . . . . . . . . 15 4.1.3 RBMs in Computer Vision . . . . . . . . . . . . . . . . . 17 4.2 Convolutional Restricted Boltzmann Machines (CRBMs) . . . . . 17 4.2.1 Cross-Correlation and Convolution . . . . . . . . . . . . 18 4.2.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2.3 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2.4 Limitations of the CRBM . . . . . . . . . . . . . . . . . 24 5 The Space-Time Deep Belief Network . . . . . . . . . . . . . . . . . 25 5.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Training and Inference . . . . . . . . . . . . . . . . . . . . . . . 27 6 ST-DBN as a Discriminative Feature Extractor . . . . . . . . . . . . 31 6.1 Measuring Invariance . . . . . . . . . . . . . . . . . . . . . . . . 31 6.1.1 Dataset and Training . . . . . . . . . . . . . . . . . . . . 31 6.1.2 Invariance Measure . . . . . . . . . . . . . . . . . . . . . 32 6.2 Unsupervised Feature Learning for Classification . . . . . . . . . 34 6.2.1 Dataset and Training . . . . . . . . . . . . . . . . . . . . 34 6.2.2 Classification Performance . . . . . . . . . . . . . . . . . 36 6.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 37 7 ST-DBN as a Generative Model . . . . . . . . . . . . . . . . . . . . 38 7.1 Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 7.2 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 8 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . 41 8.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 8.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 iv List of Tables Table 2.1 A comparison of selected computational models that mimic the human visual system. Each column is a desirable property. The “Invariant” column shows the type of transformations to which the system is invariant. “G”,“T” , “S” and “R” denote generic transformations, translation, scale and rotation, respectively. . 10 Table 6.1 Average classification accuracy results for KTH actions dataset. 36 v List of Figures Figure 4.1 Computational flow of feature extraction using a CRBM for one input frame. Directed edges shows the feedforward com- putational flow. The underlying generative model has undi- rected edges. . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Figure 5.1 Illustration of the flow of computation in a ST-DBN feature extractor. ST-DBN aggregates spatial information, shrinking the resolution in space, and then aggregates over time, shrink- ing the resolution in time, and repeats until the representation becomes sufficiently small. . . . . . . . . . . . . . . . . . . . 26 Figure 5.2 Feature extraction in the spatial pooling layer for an input video with nVt frames. Each input frame is fed into a CRBM. . . . 27 Figure 5.3 Feature extraction in the temporal pooling layer. Each pixel sequence is fed into a CRBM. . . . . . . . . . . . . . . . . . 28 Figure 6.1 Invariance scores for common transformations in natural videos, computed for layer 1 (S1) and layer 2 (S2) of a CDBN and layer 2 (T1) of ST-DBN. (Higher is better.) . . . . . . . . . . 33 Figure 6.2 Learned layer one and layer two ST-DBN filters on KTH. For the first layer (left), each square shows a 8×8 spatial filter. For the second layer (right), each row shows a temporal filter over 6 time steps. . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Figure 7.1 Denoising results: (a) Test frame; (b) Test frame corrupted with noise; (c) Reconstruction using 1-layer ST-DBN; (d) Re- construction with 2-layer ST-DBN. . . . . . . . . . . . . . . 39 vi Figure 7.2 Top video shows an observed sequence of gazes/foci of atten- tion (i.e., frames 2-4). Bottom video shows reconstructions within the gaze windows and predictions outside them. . . . . 40 vii Acknowledgments I would like to express my gratitude to many people, who have made me a better researcher and without whom this thesis would not have been written: most of all, my supervisor Nando de Freitas, for his invaluable guidance, continuing support and infinite optimism throughout the ups and downs of my degree. Jo-Anne Ting, for her helpful feedbacks on my thesis and for being the best communicator and collaborator I have ever met. Doctor Benjamin Marlin, professors Jim Little, David Lowe, Greg Mori and Kevin Murphy for their rewarding discussions and brilliant advice. The wonderful people at UBC and SFU: Eric Brochu, David Duvenaud, Jiawei Huang, Emtiyaz Khan, Nimalan Mahendran, Kevin Swersky and Haiyang Wang, just to name a few, for sharing their clever ideas or challenging problems with me. And last but not least, my parents Hong Luo and Hongji Chen, for their deep and invariant love that travels through the vast space across the Pacific Ocean and lasts through the unforgettable time of my Master’s degree. viii Chapter 1 Introduction Unsupervised feature learning is a challenging but crucial step towards building robust visual representations. Such representations can be used for a myriad of tasks from low-level ones like image denoising, inpainting and reconstruction to higher-level ones like object and activity recognition. A general consensus on the desirable properties of a feature extractor has emerged from the body of literature on neuroscience and unsupervised feature learning, and these include the follow- ing: i. hierarchical distributed representation (for memory and generalization efficiency [4, 29]) ii. feature invariance (for selectivity and robustness to input transformations [15, 23, 53]) iii. generative properties for inference (e.g., denoising, reconstruction, predic- tion, analogy-learning [19, 29, 32]) iv. good discriminative performance (for object and action recognition [8, 33]) v. at the expense of re-iterating, unsupervised learning of features because of the lack of supervisory signals in many sources of data. In addition, learning from large-scale spatio-temporal data such as videos can be computationally intensive and presents yet another challenge. We propose a hierarchical, distributed probabilistic model for unsupervised learning of invariant spatio-temporal features from video. Our work builds upon 1 recently proposed deep learning approaches [5, 19, 41]. In particular, we adopt the convolutional Restricted Boltzmann machine (CRBM), previously introduced for static images [9, 29, 35], as the basic building block in the architecture due to its previously reported success [29]. Our architecture, called the Space-Time Deep Belief Network (ST-DBN), ag- gregates over both time and space in an alternating fashion so that higher layers capture more distant events in space and time. As a result, even if an image patch changes rapidly from frame to frame, objects in the video vary slowly at higher levels of the hierarchy. 1.1 Thesis Contribution Our model fulfills all the desirable properties listed above. We build feature in- variance in a similar fashion to approaches such as Slow Feature Analysis [53] and Hierarchical Temporal Memory [16]. However, the nodes of our hierarchy learn and encode distributed feature representations (having higher complexity in higher layers of the hierarchy). Our model is generative and, hence, able to deal with occlusions in video and to make predictions that account for image content and transformations. Moreover, it extracts features that lend themselves naturally to discriminative tasks such as object and motion recognition. 1.2 Thesis Organization This thesis is organized as follows: in chapter 2 we review computational principles of the human visual system to provide biological motivation for our work. Then we give an overview of feature extraction techniques from spatiotemporal data in chapter 3. In chapter 4 we review CRBMs, and in chapter 5 we introduce the proposed model, STDBN, which composes CRBMs into a multilayered model for videos. The STDBN can be used both in a discriminative setting and in a generative setting, which we investigate in chapter 6 and chapter 7, respectively. Finally we discuss the limitations and extensions of STDBN and conclude in chapter 8. 2 Chapter 2 Principles and Models of the Human Visual System Despite continuous progress in the design of artificial visual systems, the human visual system remains the most successful one in a myriad of challenging visual tasks. This success can be largely attributed to the visual system’s neuronal rep- resentation of the visual world. In this chapter, we provide a general overview of three learning principles of the human visual system and discuss a few of the computational models that partially implement these principles. 2.1 Principles of the Vision Cortex Here we outline three of the most well-recognized principles of the visual cortex: hierarchical representations, invariance and sparseness. 2.1.1 Hierarchical Representations The first and the most important principle of the visual cortex is a hierarchical structure for computation and information. The connectivity of the visual cortex suggests that visual information is processed in a deep hierarchy: for example, the ventral pathway, which is where object recognition is done, follows the path which consists of areas such as the retina, lateral geniculate nucleus (LGN), visual area 3 1(V1), visual area 2 (V2), visual area 4 (V4), and inferotemporal area (IT) [52]1. There is also an abstraction hierarchy. For example, in LGN, there is a point-to- point connectivity that reflects the light intensity of every location in the retina. As we proceed up the hierarchy, higher layers compose information from layers below to form more abstract concepts, gradually losing the point-to-point connec- tivity. Eventually at IT, neuron firings become insensitive to the location of input signals. The final aspect of the hierarchical organization is that layers across the hierarchy have a common learning module. As an example, the visual cortex com- prises a large number of micro-columns that share a common, six-layered structure. These micro-columns are grouped into visual areas by sets of dominating external connections [34]. The hierarchical organization principle provides memory and generalization efficiency [4]. It is memory efficient because the neuronal representation can de- scribe complex objects in terms of its simpler components. Similarly, hierarchical composition of previously learned features leads to novel feature generation, allow- ing for efficient generalization without having to learn new features “from scratch”. 2.1.2 Invariance The second principle of the human visual system is its ability to recognize an ob- ject despite variations in the object’s presentation. Special types of neurons called “complex cells” are found to fire for a visual stimulus largely independent of its location [20]. Similarly, neuronal responses that are invariant to scale [21, 37] and viewpoint [6] are discovered experimentally. More astonishingly, it has been re- ported in a study [39] that there probably exist some neurons that fire for concepts such as Halle Berry. That is, these neurons are triggered by photographs of Halle as well as her name in text. Although it is inconclusive as to whether these cells fire exclusively for the concept of “Halle Berry”, the study nonetheless demonstrates the robustness of neuronal encoding. Invariance is a highly desirable property, but it is debatable how it is learned in the visual cortex. One conjecture is that temporal coherence [3, 13, 53] is the key 1This only gives a coarse approximation to the true neuronal connectivity and the actual infor- mation processing pathway. Some connections, such as the shortcut between V1 and V4, are not modeled. 4 to learning invariant representations. Temporal coherence assumes that in natural videos, successive frames are likely to contain the same set of concepts — hence, the neuronal representation should change slowly in time. Since sequential data is abundant, learning from temporal coherence in sequential data provides an rich source of learning signal, and is believed to be more biologically plausible [17]. 2.1.3 Sparseness The third principle of the human visual system is sparsity. When natural images are presented to primates, only a small portion of the neurons in V1 are activated [49]. This property is also true for the auditory system [10] and the olfactory system [38]. Sparse coding is advantageous because the resultant neuronal coding is inter- pretable and energy efficient [2]. Interpretability means that neurons tend to fire to represent the presence of common structures, i.e. features, in natural images. Hence, it is easy to read off the content of an image by probing the population of stimulated neurons. Sparsity also reduces the total energy consumption of the visual cortex. Neuronal firing consumes energy, hence the brain can not afford continuous stimulation of a large portion of its neurons [2]. 2.2 Models Inspired by the Visual Cortex There is a significant number of models that implement one or a few of the above- stated principles. Hence, an extensive review is impractical. Instead, we describe a few relevant ones, with emphasis on unsupervised models in the machine learning community. Before reviewing the models, we describe common strategies to implement the three principles of neuronal representations mentioned in Sec. 2.1. The hierarchical organization principle is implemented by stacking some basic learning modules on top of each other to form a multi-layered network. The invariance principle is im- plemented by either hand-wiring the model structure to account for specific types of transformations or learning temporally coherent representations from videos. One of the most typical model structure is the combination of convolution and resolution reduction. Convolution, which will be discussed in detail in Sec. 4.2, assumes that local image statistics can be replicated globally in the image and is 5 advantageous for building efficient, scalable visual systems. Resolution reduction ignores small shifts of the local image statistics and provides translational invari- ance. Finally, the sparsity principle is implemented by imposing sparsity-inducing priors or regularizers on the system’s representation of images. 2.2.1 The Simple-Complex Cell Model The simple-complex cell model [20] is one of the earliest computational models of V1 cells. It can be used to account for invariance at earlier stages of visual process- ing. Two types of cells, simple and complex, are tuned to oriented edges. Simple cells are directly connected to input image pixels, and each cell is selective for a specific orientation. Each complex cell pools over a region of simple cells tuned to similar orientations and fires as long as any of the pooled simple cells fires. This mechanism allows the complex cells to be locally rotational invariant. However, generalizing the invariance property to other transformations such as scaling and translation would need to be done in a brute-force manner by enumerating all cases of variations. Consequently, the number of simple cells as well as the size of the pooling regions of complex cells would become overwhelmingly large. The difficulty in generalization for the Simple-Complex cell model is allevi- ated by the hierarchical principle. The HMAX model [42] constructs a multi- layered simple-complex cell model where complex cells of one layer are wired as inputs to the simple cells of the layer above. This structure enables a rich class of transformations decomposed and handled separately at each layer. For example, long-range translation can be decomposed into successive local translations, and as the signals progress to higher layers in the hierarchy, the representation is in- variant over a longer range of translations. The invariance hierarchy argument is applicable to other complex transformations and is one of the core ideas for most deep models. However, HMAX has its limitations. Not only are the parameters mostly set by hand, but the connectivity between simple and complex cells needs to be manually specified for invariance properties to hold given transformations. A better strategy would be to learn the parameters and the connectivity from the data of interest. 6 2.2.2 Sparse Coding Sparse coding [36] is a popular generative model of the V1 neurons which empha- size the learning rather than the invariance aspect of the neuronal representation. Parameters in the model are completely learned from the data of interest. The model constructs an over-complete set of continuous neurons, i.e. the number of neurons is greater than the number of pixels in the image. Each neuron represents an image feature detector. Then the neurons are linearly combined to restore the input image. The neuronal firing pattern is regularized to be sparse for each image. On natural images, sparse coding leads to learned features resembling Gabor filters at different orientations, scales and locations. Sparse coding, as is, does not account for invariance and generalization effi- ciency of the neuronal representation. One improvement [7] proposes a factorized hierarchical model of videos where the sparse representations of a sequence of im- ages are separated into two parts, the amplitude and phase (or correspondingly, “form” and “motion”). The amplitude is stable over time, and the phase dynamics are captured by a time-series model that linearizes the first derivative of the phase sequence. Another improvement applies sparse coding convolutionally in a hierarchical generative model [54]. The hierarchical model learns Gabor-like features in the first layer and complex shape-features in higher layers, and achieves state-of-the- art performances on the Caltech-101 dataset. 2.2.3 Feedforward Neural Networks The feedforward neural network is another model class that mimics the visual cor- tex. A neural network is a network of computational neurons connected via di- rected links, where each individual neuron integrates information and returns a firing potential, and collectively the network of neurons are capable of performing complex, intelligent tasks through learning. Neural network models that implement the hierarchical and invariant represen- tation include the Neocognitron [14], Convolutional Neural Net (CNN) [27] and the Siamese Convolutional Net [33]. All of them are multi-layered networks with a chain of alternating convolution and pooling phases. Computation-wise, each layer 7 searches for a specific pattern within a local region in the input image and returns the best match to the layer above where the same computation is repeated. The Siamese Convolutional Net further leverages temporal coherence and enforces the hidden representations for adjacent images in a video to be similar and otherwise different. These networks demonstrate nice invariance properties in recognizing hand-written digits, faces, animals, etc. Despite reported success, training feedforward neural networks can be rather difficult. This difficulty is largely due to most network’s dependance on supervised learning. Typically, supervisory signals are rare and low dimensional in contrast with the large set of parameters. 2.2.4 Undirected Neural Networks The difficulty in parameter estimation of feedforward nets has caused researchers to shift attention towards undirected neural networks, a model class that is similar in flavor, but easier to train compared to their feedforward counterparts. Undirected neural networks are generative models where neurons are connected via undirected links. Learning is unsupervised and makes use of unlabeled data, which is mostly abundant and high dimensional in computer vision. The parameters of a undirected neural network are typically used to initialize a directed network, which is then fine-tuned with supervisory signals. The simplest undirected neural network is the Restricted Boltzmann Machine (RBM) [19], which is a shallow latent-variable model with restricted connections within both the latent variables (neurons) and the visible variables. RBMs will be discussed in detail in Chapter 4. Derivatives of the RBMs following the three principles of the human visual cortex are as follows. • Deep Belief Network (DBN) A DBN [18] is a multi-layered generative net- work where the top-most layer is an RBM, and the layers below are directed neural networks with top-down connections from the layer above. The RBM models associative memory that generates a set of compatible abstract con- cepts, and the directed networks synthesize an image deterministically by realizing the concepts. Parameter estimation of DBNs typically follows a layer-wise greedy procedure, which is reviewed in Chapter 5. 8 • Deep Boltzmann Machine (DBM) A DBM [43] is a complete hierarchi- cal generalization of the RBM. Each layer of the DBM is an RBM. Every neuron in the intermediate layers incorporates both top-down and bottom-up connections. In addition, parameters can be jointly optimized in an unsu- pervised manner. Unfortunately this procedure remains too expensive for large-scale vision problems. • Convolutional Deep Belief Network (CDBN) A CDBN [28, 35], which will be discussed in Chapter 4, is an undirected counterpart of the Neocog- nitron. It hand-specifies translational invariance and sparsity into the sys- tem and is able to learn features resembling object parts from the Caltech- 101 [12] dataset. One limitation of this model and many others is their at- tempts to model the intensity of continuous-valued pixel values but not the interaction among them. • Mean-Covariance Restricted Botlzmann Machine (mcRBM) A mcRBM [40] is an 3-way factored RBM that models the mean and the covariance structure of images. A three-way factor RBM allows the RBM to model interactions between two sets of visible units efficiently. When applied to the covariance structure, it automatically provides illuminance invariance because covariances only depend on relative intensity levels between pix- els. Equivalently, the model can be thought of as a two-layer DBN where the first layer performs feature detection, and the second layer models the squared responses with an RBM. Additionally, the parameters are learned in a similar, layer-wise fashion. That is, the second layer starts learning only after the first layer converges. From the CIFAR10 object recognition dataset, the first layer learns edge-like features, and the second layer, initialized us- ing a topography over the output of the first layer, learns to be selective to a set of edges of similar orientations, scales, and locations. A deep network is constructed by applying mcRBMs convolutionally in a hierarchy, giving state-of-the-art performance on CIFAR10. However, the mcRBMs in the deep network are not trained convolutionally, so the network is a generative model only for small patches and not scalable to large images. 9 2.3 Summary Finally, Table 2.1 below summarizes the models described in this section, showing whether each satisfies the three principles of the human visual system and how applicable each is to large-scale computer vision applications. Table 2.1: A comparison of selected computational models that mimic the hu- man visual system. Each column is a desirable property. The “Invariant” column shows the type of transformations to which the system is invari- ant. “G”,“T” , “S” and “R” denote generic transformations, translation, scale and rotation, respectively. Model Hierarchical Sparse Invariant Scalable Unsupervised Simple-Complex N N G N N HMAX Y N G Y N Sparse Coding N Y N N Y Factorized Sparse Coding Y Y G N Y Deconvolutional Network Y Y T Y Y Neocognitron Y N T Y Y CNN Y N T Y N Siamese CNN Y N G Y N RBM N N N N Y mcRBM Y N T, S, R N Y DBN, DBM Y N N N Y CDBN Y Y T Y Y It can be seen from Table 2.1 that existing models only partially satisfy the three principles of human visual cortex, and not all of them are directly applicable to building large vision systems. The key contribution of the model proposed in this thesis is that it is not only hierarchical, sparse and invariant to generic trans- formations, but also scalable and learnable in an unsupervised fashion from video. In the next chapter, we review unsupervised feature learning from spatio-temporal data such as video. 10 Chapter 3 Feature Extraction from Video In this chapter we review computational models that extract features from spatio- temporal data. The extraction of video representation has received considerable attention in the computer vision community and is mostly applied to action/activity recognition tasks. Video representation for action/activity recognition systems typ- ically relies on handcrafted feature descriptors and detectors. The popular pipeline for such systems follows the sequence of pre-processing, feature detection, feature description and classification based on the feature descriptors. First, video data are pre-processed to eliminate irrelevant factors of the recogni- tion task. For instance, local contrast normalization is a popular preprocessing step that normalizes the intensity of a pixel by a weighted sum of its spatio-temporal neighbors so as to eliminate local illumination variation. Second, spatio-temporal detectors select locally “salient” regions and help to reduce the amount of data for consideration. Existing spatio-temporal detectors can be classified into two categories: i) detectors that treat time no differently than space and ii) detectors that decouple space and time. The first category extends successful 2D detectors into space-time. For example, the 3D HMAX [22] defines saliency based on spatio-temporal gradients. The Space-Time Interest Point [25] detector and the 3D Hessian [51] detectors are based on the spatio-temporal Hes- sian matrix. The second category extracts the 2D spatial structures before the spatio-temporal structures. For example, the Cuboid detector [11] applies a 2D Gaussian filter in space and then a 1D Gabor filter in time. Since each method de- 11 fines saliency differently, it is unclear which definition serves best for a given task. Alternatively, we also use dense sampling to extract regularly-spaced or randomly- positioned blocks in space-time. After the salient locations are highlighted, a feature descriptor is applied to summarize the motion and shape within a neighborhood block of the highlighted locations. Most descriptors use a bag-of-words approach and discard the posi- tion of the blocks. For example, the Histogram of spatio-temporal Gradient/Flow (HOG/HOF) [26] concatenates the histograms of gradient and optic flow. The 3D SIFT descriptor [1] splits a block into non-overlapping cubes and concatenates histograms of gradient of the cubes. Some descriptors take into account longer temporal context. For example, the recursive sparse spatio-temporal coding ap- proach [8] recursively applies sparse coding to longer video block centered at the locations returned by the Cuboid detector. The Gated Restricted Boltzmann Ma- chine [46] models the temporal differences between adjacent frames using a con- ditional RBM[47] and models longer sequences using sparse coding. Finally, once the descriptors are computed, they are fed into a classifier, typi- cally a K-means classifier or a Support Vector Machine (SVM) for discriminative tasks. The models described above are limited in the sense that (i) most feature de- scriptors and detectors are specified manually and (ii) models that do learn their parameters still treat videos as bags of independent space-time cubes and discard global information. In this thesis, we introduce a model that seeks to incorporate both local and global spatio-temporal information in a hierarchical structure and learns its parameters in an unsupervised fashion from the data of interest. In the next chapters, we describe the building blocks for the proposed model, show how the resulting spatio-temporal model is developed and demonstrate its discrimina- tive and generative properties. 12 Chapter 4 Convolutional Restricted Boltzmann Machines In this chapter, we review the Restricted Boltzmann Machine (RBM) and the con- volutional Restricted Boltzmann Machine (CRBM). 4.1 Restricted Boltzmann Machines 4.1.1 Model An RBM is a bipartite hidden-variable model. It consists of a set of visible units v and a set of hidden units h. The units are not connected within each of the two sets but are otherwise fully connected with weights W . In addition, a real-valued offset term is added to each unit to account for its mean. The offsets for v and h are called b and d, respectively. We use θ to denote the collection of parameters W ∈ RnV×nH ,b ∈ RnH and d ∈ RnV , where nV is the number of visible units and nH is the number of hidden units. A model’s preference of one pair of v and h over another is defined by an energy function E(v,h,θ), which gives low values to favored pairs. The joint distribution of a pair of v and h conditioning on θ is given by exponentiating and normalizing the energy function over all possible pairs, and the data likelihood conditioning on θ is obtained by integrating out h: 13 p(v,h|θ) = 1 Z(θ) exp(−E(v,h,θ)) Z(θ) = ∫ v′∈V,h′∈H exp(−E(v′,h′,θ)) p(v|θ) = exp(−F(v,θ))∫ v′∈V exp(−F(v′,θ)) F(v,θ) =− log ∫ h∈H exp(−E(v,h,θ)) where Z(θ) is the called the normalization constant, F(v,θ) is called the free en- ergy, and V and H denote the domain of v′ and h′, respectively. For the scope of this thesis, the units in h are restricted to be binary, i.e. H = {0,1}nH , while units in v can be either binary or continuous. In the case of binary visible units (V= {0,1}nV ), the energy function is defined as: E(v,h,θ) =− nV ∑ i=1 nH ∑ g=1 viWi,ghg− nH ∑ g=1 hgbg− nV ∑ i=1 vidi The conditionals p(h|v,θ) and p(v|h,θ) are given in the following factorized form: p(vi = 1|h,θ) = σ ( nH ∑ g=1 Wi,ghg+di ) p(hg = 1|v,θ) = σ ( nV ∑ i=1 viWi,g+bg ) (4.1) where σ(x) = 11+exp(−x) is the sigmoid function. The free energy is: F(v,θ) =− nV ∑ i=1 vidi− nH ∑ g=1 log ( 1+ exp ( nV ∑ i=1 viWi,g+bg )) In the case of continuous visible units (V= RnV ), one popular energy function 14 is: E(v,h,θ) =− 1 σ2V ( nV ∑ i=1 nH ∑ g=1 viWi,ghg+ nH ∑ g=1 hgbg+ nV ∑ i=1 vidi− nV ∑ i=1 v2i 2 ) where σV is the standard deviation for all visible units. The conditionals are now given by: p(vi|h,θ) =N ( nH ∑ g=1 Wi,ghg+di,σ2V ) p(hg = 1|v,θ) = σ ( 1 σ2V ( nV ∑ i=1 viWi,g+bg )) (4.2) The free energy is: F(v,θ) = nV ∑ i=1 ( v2i 2σ2V − vidi σ2V ) − nH ∑ g=1 log ( 1+ exp ( bg+∑nVi=1 viWi,g σ2V )) In both cases, the bipartite connectivity allows not only v and h to be condi- tional independent of each other, but also their conditionals to take a factorized form. Both of these properties prove convenient for inference. 4.1.2 Inference and Learning The independent, factorized conditional distribution of both v and h allows Gibbs sampling and stochastic gradient approximations to be computed efficiently. First, we describe the sampling procedure as below: • Randomly initialize v to some state v′. • Alternating from sampling h′ ∼ p(h|v′,θ) and sampling v′ ∼ p(v|h′,θ) until both v′ and h′ converge. • {v′,h′} is an unbiased sample. Second, we review the ideal maximum likelihood solution to learning the pa- rameters θ , followed by a computationally-feasible approximation. Given a data 15 set V = {v(t)}Tt=1, the maximum likelihood gives the following estimation for θ : θML = argmax θ LML(θ) = argmax θ log T ∏ t=1 p(vt |θ) The exact but intractable gradient for maximum likelihood is given by: ∂LML(θ) = T ∑ t=1 ∂ log ∫ h∈H exp(−E(v,h))−T∂ logZ(θ) The above gradient is approximated by Contrastive Divergence (CD), as fol- lows: ∂LML(θ)≈ ∂LCD(θ) = T ∑ t=1 ( ∂ logexp(−E(v(t),h(t)))−∂ logexp(−E(ṽ(t), h̃(t))) ) (4.3) where h(t) is sampled conditioning on the data v(t), i.e. from p(h|v(t),θ), and {ṽ(t), h̃(t)} is a sample obtained by running Gibbs Sampling starting from the data v(t) for k steps. The value of k is typically small, say, 1. For binary data, the CD gradient for W,b and d are: ∂CD(θ) ∂Wig = T ∑ t=1 ( v(t)i h (t) g − ṽ(t)i h̃(t)g ) ∂CD(θ) ∂bg = T ∑ t=1 ( h(t)g − h̃(t)g ) ∂CD(θ) ∂di = T ∑ t=1 ( v(t)i − ṽ(t)i ) For continuous data, the CD gradient is identical to the one above up to a con- stant multiplicative factor 1σ2V 1, which can be merged into the step length parameter in most gradient based algorithms. More details on stochastic algorithms for up- dating the weights can be found in [31, 45]. 1 In this thesis, σV is estimated in advance, so during the CD learning it can be treated as a constant. See [24] for reference that learns σV simultaneously with θ 16 4.1.3 RBMs in Computer Vision When RBMs are applied in Computer Vision, the visible units typically corre- sponds to individual image pixels, and the hidden units can be interpreted as inde- pendent feature detectors. This is because the conditional distribution of each and every hidden unit depends on, as indicated in Eqs. (4.1) and (4.2), the dot product between the input image and a filter, which is a column in W associated with the hidden unit. Therefore columns of W are also known as the “filters” or “features”. Finally, due to the RBM’s similarity to the human neural system, hidden units are often referred to as “neurons”, and their conditional probabilities of being on (1) are referred to as “firing rates” or “activation potentials”. However, the RBM has a few limitations for modeling images. First, it is difficult to use plain RBMs for images of even a modest size (e.g. 640×480). This is because the number of parameters in W scales linearly in the size (i.e. number of pixels) of the image, and that the number of hidden units also grows due to increasing input complexity. The resultant large parameter space poses serious issues for learning and storage. Second, RBMs are too generic to take advantage of the intrinsic nature of image data. More specific models will leverage the fact that images have a two-dimensional layout and that natural images are invariant to transformations such as translation, scaling and lighting variation. One improved model, as we review in the next section, is the a convolutional derivative of the RBM. 4.2 Convolutional Restricted Boltzmann Machines (CRBMs) In this section we review the Convolutional Restricted Boltzmann Machines, a natural extension of the generic RBMs to account for translational invariance. In natural images, a significant amount of the image structures tends to be localized and repetitive. For example, an image of buildings tends to have short, vertical edges occurring at different places. The statistical strength of local patches at different locations could be harnessed for fast feature learning. Moreover, local image structures should be detectable regardless of their location. Both of the above properties can be achieved by incorporating convolutions into RBMs. 17 4.2.1 Cross-Correlation and Convolution Given two vectors A ∈ Rm and B ∈ Rn, assuming m ≥ n, the cross-correlation, denoted with “?”, between A and B slides a window of length n along A and mul- tiplies it with B, returning all the dot products in its course. Cross-correlation has two modes, “full” and “valid”. The “full” mode allows parts of the sliding window to fall outside of A (by padding the outside with zeros) while the “valid” mode does not. More specifically, (A?v B)i = n ∑ j=1 Ai+ j−1B j ∀i = 1, . . . ,m−n+1 (A? f B)i = min(m−i+1,n) ∑ j=max(2−i,1) Ai+ j−1B j ∀i = 2−n, . . . ,m where the subscripts v and f denote the “valid” mode and the “full” mode, respec- tively. For any d-dimensional tensor A, define f lip(·) as a function that flips A along every one of its d dimensions. Then convolution, denoted with “∗”, can be defined as the cross-correlation between A and f lip(B). Likewise, convolution also has the “full” and the “valid” mode. (A∗v B)i = (A?v f lip(B))i = n ∑ j=1 Ai+ j−1Bn− j+1 ∀i = 1, . . . ,m−n+1 (A∗ f B)i = (A∗ f f lip(B))i = max(m−i+1,n) ∑ j=max(2−i,1) Ai+ j−1Bn− j+1 ∀i = 2−n, . . . ,m Therefore, the “valid” mode returns a vector of length m−n+1, and the “full” mode a vector of length m+n−1. The definitions of convolution and correlation can be trivially extended into high dimensions. 18 4.2.2 Model The CRBM, shown in Fig. 4.1, consists of 3 sets of units: visible (real-valued or binary) units v, binary hidden units h, and binary max-pooling units p. For now we assume the inputs are binary for simplicity. The visible units v ∈ A⊆ {0,1}ch×nV x×nV y are inputs to the CRBM and consti- tute a 2D image that has a spatial resolution of (nV x×nV y) and ch channels (e.g., 3 channels for RGB images, 1 for grayscale ). The hidden units h are partitioned into groups and each group receives con- volutional2 connections from v. In other words, each hidden unit in each group is connected to a local region of v of a common size. The units in a given group are arranged to maintain the locations of their corresponding regions, and they share the same connection weights, a.k.a. filters. Given a set of filters W with |W | groups, each W g ∈ Rch×nWx×nWy , h also contains |W | groups, and hg has a size of (nHx×nHy), where nHx = nV x−nWx+1 and nHy = nV y−nWy+1 (as a result of the valid convolution mode). Finally, the max-pooling units p are lower-resolution version of h, and used as output representations of the CRBM. p has the same number of groups as h. For a given group, hg is partitioned into non-overlapping blocks Bg = {Bg1,Bg2, ...,Bg|B|}, and each block corresponds to one unit in pg. If each block has a size of (nBx× nBy), then pg has dimensions (nPx×nPy), where nPx = nHx/nBx and nPy = nHy/nBy (assuming that nBx divides nHx and nBy divides nHy evenly for simplicity). Formally, the set of parameters in the CRBM is θ = {W,b,d}. From now on, to avoid notation clutter, θ will be implicitly conditioned on and omitted in the expressions. W ∈ Rch×nWx×nWy×|W | is the set of filters weights. b ∈ R|W | is the vector of bias terms, where each scalar term bg is associated with the g-th group of hidden units hg. d ∈ Rch is another vector of bias terms where each scalar term dc is associated with all the visible units in channel c. The The energy function of the CRBM is 2We use the valid convolution mode. 19 Figure 4.1: Computational flow of feature extraction using a CRBM for one input frame. Directed edges shows the feedforward computational flow. The underlying generative model has undirected edges. defined as follows (we present a more interpretable form later): E(v,h) =− |W | ∑ g=1 nH ∑ r,s=1 nW ∑ i, j=1 hgr,s ch ∑ c=1 W gc,i, jvc,i+r−1, j+s−1− |W | ∑ g=1 bg nH ∑ r,s=1 hgr,s− ch ∑ c=1 dc nV ∑ i, j=1 vc,i, j subject to ∑ r,s∈Bgα hgr,s+(1− pgα) = 1,∀g,∀α = 1, .., |Bg| (4.4) where hgr,s and vc,i, j are hidden and visible units in hg and v, respectively, and pgα represents the max-pooled unit from the Bgα block in the hidden layer hg below. For ease of interpretability, we define “•” to be the element-wise multiplica- tion of two tensors followed by a summation, Eq. (4.4) can be re-written in the 20 following form: E(v,h) = |W | ∑ g=1 ch ∑ c=1 E(vc,hg) subject to ∑ r,s∈Bgα hgr,s+(1− pgα) = 1,∀g,α = 1, .., |B| E(vc,hg) =−hg • (vc ?v W gc )−bg nH ∑ r,s=1 hgr,s−dc nV ∑ i, j=1 vc,i, j The conditional probability distribution of v can then be derived [29]: P(vc,i, j = 1|h) = σ ( dc+ |W | ∑ g=1 nW ∑ r,s=1 W gc,r,sh g i−r+1, j−s+1 ) = σ ( dc+ |W | ∑ g=1 (hg ∗ f W gc )i, j ) (4.5) The conditional probability of h and p comes from an operation called “prob- abilistic max pooling” [29]. Due to the sum-to-one constraint in Eq. (4.4), each block Bgα of hidden units and the negation of the corresponding max-pooling unit pgα can be concatenated into a 1-of-K categorical variable HP g α . We use {HPgα = hgr,s} to denote the event that hgr,s = 1, pgα = 1, and hgr′,s′ = 0,∀{r′,s′} 6= {r,s} ∈ Bgα , and use {HPgα = ¬pgα}to denote the event that all units in Bgα and pgα are off. The conditionals can be derived as: P(HPgα = hgr,s|v) = exp(I(hgr,s)) 1+∑{r′,s′}∈Bgα exp(I(h g r′,s′)) (4.6) P(HPgα = ¬pgα |v) = 11+∑r,s∈Bgα exp(I(h g r,s)) (4.7) 21 where I(hgr,s) = bg+ nW ∑ i, j=1 ch ∑ c=1 W gc,i, jvc,i+r−1, j+s−1 = bg+ ch ∑ c=1 (vc ?v W gc )r,s For continuous inputs, the conditional distribution of v and h are very similar to Eqs. (4.5) and (4.6) except for an adjustment of the standard deviation σV : P(vc,i, j = 1|h) =N ( dc+ |W | ∑ g=1 (hg ∗ f W gc )i, j,σ2V ) I(hgr,s) = 1 σ2V ( bg+ ch ∑ c=1 (vc ?v W gc )r,s ) with the rest staying the same. 4.2.3 Training We train CRBMs by finding the model parameters that minimize the energy of states drawn from the data (i.e., maximize data log-likelihood). Since CRBMs are highly overcomplete by construction [29, 35], regularization is required. As in [28], we force the max-pooled unit activations to be sparse by placing a penalty term so that the firing rates of max-pooling units are close to a small constant value r. Given a dataset of T i.i.d. images {v(1),v(2), . . . ,v(T )}, the problem is to find the set of parameters θ that minimizes the objective: − T ∑ t=1 logP(v(t))+λ |W | ∑ g=1 [ r− ( 1 T |Bg| T ∑ t=1 |Bg| ∑ α=1 P(HPgα = ¬pgα |v(t)) )]2 (4.8) = T ∑ t=1 − logP(v(t))+λLsparsity where λ is a regularization constant, and r is a constant that controls the sparseness of activated max-pooled units. Like an RBM, gradients of the first part of the objective is intractable to com- 22 pute and we use 1-step contrastive divergence [19] to get an approximate gradient. The CD procedure for each data point vt is: i. Use Eqs. (4.5) and (4.6) to sample h(t) given v(t) ii. Sample ṽ(t) given h(t) iii. Sample h̃(t) given ṽ(t) The CD gradient for a binary CRBM is given by: ∂LCD = T ∑ t=1 ( ∂ (−E(v(t),h(t)))−∂ (−E(ṽ(t), h̃(t))) ) where the partial gradients of the energy function with respect to the parameters are given by: ∂ −E(v,h) ∂W gc,i, j = (vc ?v hg)i, j ∂ −E(v,h) ∂bg = nH ∑ r,s=1 hgr,s ∂ −E(v,h) ∂dc = nV ∑ i, j=1 vc,i, j Similarly, the gradients for a gaussian CRBM are identical to those of a binary CRBM except for a multiplicative factor, which is absorbed into the step length parameter of most gradient based methods. We also only update the hidden biases bg to minimize the regularization term Lsparsity in Eq. (4.8), following [29]: ∂Lsparsity ∂bg = 2 |Bg|T [ r− ( 1 |Bg|T T ∑ t=1 |Bg| ∑ α=1 P(HPgα = ¬pgα |v(t)) )] T ∑ t=1 |Bg| ∑ α=1 P(HPgα = ¬pgα |v(t))(1−P(HPgα = ¬pgα |v(t))) (4.9) A practical issue that arises during training is the effect of boundaries [35] 23 on convolution. If the image has no zero-padded edges, then boundary visible units will have fewer connections to hidden units than interior visible units. The connectivity imbalance will cause filters to collapse into the corner regions in order to reconstruct the boundary pixels well. To alleviate this problem, we pad a band of zeros, having the same width as the filter, around the image. 4.2.4 Limitations of the CRBM The CRBM exploits the 2-D layout of images as well as the statistical strength of repetitive local image features, and it has translation invariance hand-wired into the model. However, it assumes that images are independently distributed, hence is inadequate for modeling temporal structures in videos of natural images. In the next chapter, we introduce the proposed model that models both space and time in an alternating fashion and is more invariant to generic transformations. 24 Chapter 5 The Space-Time Deep Belief Network In this chapter we introduce the Space-Time Deep Belief Network, which com- poses the CRBMs in space and time into a deep belief network. The Space-Time Deep Belief Network takes a video as input and processes it such that each layer up in the hierarchy aggregates progressively longer-range patterns in space and time. The network consists of alternating spatial and temporal pooling layers. Fig. 5.1 illustrates the structure. 5.1 Model Fig. 5.2 shows the first layer of the ST-DBN—a spatial pooling layer—which takes an input video of nVt frames {v(0),v(1), ...,v(nVt)}. At every time step t, each spatial CRBM takes an input frame v(t) of size (ch× nV x× nV y) and outputs a stack p(t) of size (|W | × nPx× nPy), where W is the set of image filter (defined in Sec. 4.2) shared across all spatial CRBMs. Each p(t) is a stack of the |W | max-pooling units in the spatial CRBM, i.e., the topmost layer of sheets in Fig 4.1. The second layer of the network is a temporal pooling layer, which takes the low-resolution image sequence {p(0),p(1), ..,p(nVt)} from the spatial pooling layer and outputs a shorter sequence {s(0),s(1), ...,s(nSt)}. Fig. 5.3 shows that each pixel at location (i, j) in the image frame is collected over time to form a temporal sequence 25 Figure 5.1: Illustration of the flow of computation in a ST-DBN feature ex- tractor. ST-DBN aggregates spatial information, shrinking the resolu- tion in space, and then aggregates over time, shrinking the resolution in time, and repeats until the representation becomes sufficiently small. sIi j of size (|W | × nVt × 1). Each sIi j is fed into a CRBM, which convolves the temporal sequence sIi j with temporal filter W ′. Similar to the spatial CRBM in Sec. 4.2, the temporal CRBM uses a set of filters W ′ ∈ R|W |×nWt×1×|W ′|, where the g-th temporal filter W ′g has size (|W | × nWt × 1). However, unlike the spatial CRBM, the temporal CRBM max-pools only over one dimension, i.e., time. The temporal pooling layer has a total of (nPx×nPy) CRBMs since every pixel in the image frame is collected over time and then processed by a CRBM. Given sIi j , a temporal sequence of the (i, j)-th pixel, the temporal CRBM outputs sOi j , a stack of shorter sequences. sOi j has a size of (|W ′|×nSt ×1), where nSt ≤ nVt . The final step of the temporal pooling layer re-arranges the temporal sequence of each pixel into the original 2D spatial layout of an image frame. As seen in Fig. 5.3, the final output of the temporal pooling layer is a shorter sequence of low-resolution image frames {s(0),s(1), ...,s(nSt)}. Similar to spatial CRBMs, all 26 Figure 5.2: Feature extraction in the spatial pooling layer for an input video with nVt frames. Each input frame is fed into a CRBM. CRBMs in the temporal pooling layer share the same parameters (temporal filter weights and bias terms). The sequence {s(0),s(1), ...,s(nSt)} is passed on to subsequent higher layers for further spatial and temporal pooling. 5.2 Training and Inference The entire model is trained using greedy layer-wise pretraining [18]. More specifically, starting from the bottom layer of the ST-DBN, we train each layer using random samples from the inputs to this layer as described in Sec. 4.2.3. Then the hidden representation (max-pooling units) is computed using Eqs. (4.6) and (4.7), and re-arranged as inputs to the next layer. This procedure is repeated until all the layers are trained. Once the network is trained, we can extract features (hidden representations) from a video at any given layer, or compute video samples conditioning on the hidden representation of the data. 27 Figure 5.3: Feature extraction in the temporal pooling layer. Each pixel se- quence is fed into a CRBM. For feature extraction, we traverse up the hierarchy and compute the feedfor- ward probability of the max-pooling units using Eq. (4.7) for each layer. The con- tinuous probabilities, a.k.a. the mean-field values, of the hidden and max-pooling units are used to approximate their posterior distributions. For sampling, we first initialize the hidden and max-pooling layers with the cor- responding mean-field values, and then perform Gibbs sampling from the topmost layer. The samples are then propagated backward down the hierarchy. For each layer, the distribution of max-pooling units are inherited from the layer above, and the conditional probability of the hidden units is obtained by evenly distributing 28 the residual probability mass of their corresponding max-pooling units: P(HPgα = ¬pgα |h′) = 1−P(pgα |h′) (5.1) P(HPgα = hgr,s|h′) = 1 |Bgα | P(pgα |h′) (5.2) where h′ denotes the hidden units from the layer above, P(pgα |h′) is the top-down belief about pgα . The conditional distribution of the visible units are given by Eq. (4.5). Because of the resolution reduction introduced by the max-pooling operation, top-down information is insufficient to generate the complete details of the bottom- up input, leading to low resolution video samples. This problem can be partially alleviated by sampling from a Deep Boltzmann Machine (DBM) [43] that has the same architecture with the ST-DBN, but with undirected connections. A DBM models the joint density of all variables. We illustrate the idea with a network consisting of a spatial pooling layer {v,h,p}, followed by a temporal pooling layer {p,h′,p}′ (The temporal layer treats spatial layer’s output p as its input visible units). The set of filters, the hidden and the visible biases for the two layers are {W,b,d} and {W ′,b′,d′}, respectively. The joint energy function can be written as: E(v,h) =− |W | ∑ g=1 E(v,hg)−bg nH ∑ r,s=1 hgr,s− |W ′| ∑ g=1 E(p,h′g)−b′g |W ′| ∑ r,s=1 (h′)gr,s subject to ∑ r,s∈Bgα hgr,s+(1− pgα) = 1,∀g,∀α = 1, .., |Bg| ∑ r,s∈(B′)gα (h′)gr,s+(1− (p′)gα) = 1,∀g,∀α = 1, .., |(B′)g| The conditional probability can be derived: P(HPgα = ¬pgα |h′,v) = exp(J g α) exp(Jgα)+∑{r,s}∈Bgα exp(I g r,s) (5.3) P(HPgα = hgr,s|h′,v) = exp(Igr,s) exp(Jgα)+∑{r′,s′}∈Bgα exp(I g r′,s′) (5.4) 29 where Jgα =− |W |′ ∑ g=1 ((h′)g ∗ f (W ′)gc)α −d′c We perform Gibbs Sampling using Eqs. (5.3) and (5.4) on the entire network until convergence. The advantage of using a DBM is that sampling takes into account both the top-down and bottom-up information, hence information is pre- served. However, the drawback is that parameter learning in DBMs is rather dif- ficult. Although one can to copy the parameters of the ST-DBN to the DBM, this would cause a double-counting problem. During greedy learning of the DBN, the parameters are trained such that each layer learns to re-represent the input distribu- tion. If the same set of parameters were used in a DBM, then for each layer, both the layer above and below re-represent the input distribution, and sampling based on both would lead to conditioning on the input twice. The double-counting ef- fect accumulates as the number of Gibbs sampling steps increases. As a trade-off, when high-resolution samples are required, we use Gibbs sampling from a DBM, but only for one iteration. 30 Chapter 6 ST-DBN as a Discriminative Feature Extractor In this chapter, we evaluate the discriminative aspect of the ST-DBN with two experiments. The first experiment quantifies the invariance property of the ST- DBN features, which is indicative of their discriminative performance independent of the classifier and the classification task. The second experiment evaluates the performance of ST-DBN features in an actual, action recognition task. 6.1 Measuring Invariance 6.1.1 Dataset and Training We use natural videos from [15] to compare ST-DBNs and convolutional deep be- lief networks (CDBNs) [29], which consist of stacked layers of CRBMs. The 40 natural videos contain transformations of natural scenes, e.g., translations, planar rotations and 3D rotations, with modest variations in viewpoints between succes- sive frames. We extract the relevant transformations by downsampling the videos of typical size (640×320×200) into snippets of size (110×110×50). However, for videos with rotating objects, we randomly select 50 consecutive frames in time and crop out a 220×220 window (that is then resized to 110×110) from the cen- ter of each frame. The resulting 40 snippets are standardized on a per frame basis. 31 Finally, we split the snippets evenly into training and test sets, each containing 20 snippets. We train a 2-layer ST-DBN (a temporal pooling layer stacked above a spatial pooling layer) and a 2-layer CDBN. The ST-DBN is similar to the CDBN in terms of the involved optimization steps and structural hyperparameters that need to be set. We use stochastic approximation with two-step averaging and mini-batches1 to optimize parameters. The first layers of the ST-DBN and CDBN are the same, and we use nWx = nWy = 10 and nBx = nBy = 3 for layer 1. After cross-validation, we settled on the following hyperparameter values: 25 filters for the first layer and 64 filters for higher layers, a learning rate of 0.1, a sparsity level r of 0.01, and a regularization value λ of 1. The filter weights were initialized with white Gaussian noise, multiplied with a small scalar 0.1. For layer 2, we use nWx = nWy = 10,nBx = nBy = 3 for the CDBN and nWt = 6 with a pooling ratio of 3 for the ST-DBN. Within a reasonably broad range, we found results to be insensitive to these hyperparameter settings. 6.1.2 Invariance Measure To evaluate invariance, we use the measure proposed by [15] for a single hidden unit i, which balances its global firing rate G(i) with its local firing rate L(i). The invariance measure for a hidden unit i is S(i) = L(i)/G(i), with: L(i) = 1 |Z| ∑z∈Z 1 |T (z)| ∑x∈T (z) fi(x) G(i) = E[ fi(x)] where fi(x) is an indicator function that is 1 if the neuron fires in response to input x and is 0 otherwise; Z is the set of inputs that activate the neuron i; and T (z) is the set of stimuli that consists of the reference stimulus x with transformations applied to it. L(i) measures the proportion of transformed inputs that the neuron fires in response to. G(i) measures the neuron’s selectivity to a specific type of stimuli. 1We use an initial momentum of 0.5 for the first 2 epochs before switching to a value of 0.9. In order to fit the data in memory, we use small batch sizes—2 and 5 for spatial and temporal pool- ing layers, respectively—and train on subsampled spatio-temporal patches that are approximately 16 times larger than the filter. Despite subsampling, the patches are sufficiently large to allow for competition among hidden units during convolution. 32 For each video and hidden unit i, we select a threshold such that i fires G(i) = 1% of the time. We then select 40 stimuli that activate i the most (these are single frames for the spatial pooling layers and short sequences in the temporal pooling layers) and extend the temporal length of each stimulus both forward and backward in time for 8 frames each. The local firing rate L(i) is then i’s average firing rate over 16 frames of stimuli, and the invariance score is L(i)/0.01. The invariance score of a network layer is the mean score over all the max-pooled units. 10 15 20 25 30 35 40 S1 S2 T1 Translation 10 15 20 25 30 35 40 S1 S2 T1 Zooming 10 15 20 25 30 35 40 S1 S2 T1 2D Rotation 10 15 20 25 30 35 40 S1 S2 T1 3D Rotation Figure 6.1: Invariance scores for common transformations in natural videos, computed for layer 1 (S1) and layer 2 (S2) of a CDBN and layer 2 (T1) of ST-DBN. (Higher is better.) Since ST-DBN performs max-pooling over time, its hidden representations should vary more slowly than static models. Its filters should also be more selective than purely spatial filters. Fig. 6.1 shows invariance scores for translations, zoom- ing, and 2D and 3D rotations using layer 1 of the CDBN (S1), layer 2 of the CDBN (S2), and layer 2 of ST-DBN (T1). S1 serves as a baseline measure since it is the first layer for both CDBN and ST-DBN. We see that ST-DBN yields significantly more invariant representations than CDBN (S2 vs. T1 scores). ST-DBN shows the greatest invariance for 3D rotations—the most complicated transformation. While a 2-layer architecture appears to achieve greater invariance for zooming and 2D ro- tations, the improvement offered by ST-DBN is more pronounced. For translation, 33 all architectures have built-in invariance, leading to similar scores. We should point out that since ST-DBN is trained on video sequences, whereas the CDBN is trained on images only, that a comparison to CDBN is unfair. Nonethe- less, this experiment highlights the importance of training on temporal data in order to achieve invariance. 6.2 Unsupervised Feature Learning for Classification 6.2.1 Dataset and Training We used the standard KTH dataset [44] to evaluate the effectiveness of the learned feature descriptors for human activity recognition. The dataset has 2391 videos, consisting of 6 types of actions (walking, jogging, running, boxing, hand wav- ing and hand clapping), performed by 25 people in 4 different backgrounds. The dataset includes variations in subject, appearance, scale, illumination and action execution. First, we downsampled the videos by a factor of 2 to a spatial resolution of 80× 60 pixels each, while preserving the video length (∼ 4 sec long each, at 25 fps). Subsequently, we pre-processed the videos using 3D local contrast nor- malization. More specifically, given a 3D video V , the local contrast normalization computes the following quantity for each pixel vx,y,z to get the normalized value v′′x,y,z: v′x,y,z = vx,y,z− (V ∗ f g)x,y,z (6.1) v′′x,y,z = v ′ x,y,z/max ( 1, √ ((V ′)2 ∗ f g)x,y,z ) (6.2) where g is a (9×9×9) 3D gaussian window with a isotropic standard deviation of 4. We divided the dataset into training and test sets following the procedure in [50]. For a particular trial, videos of 9 random subjects were used for training a 4- layer ST-DBN, with videos of the remaining 16 subjects used for test. We used leave-one-out (LOO) cross-validation to calculate classification results for the 16 test subjects. For each of the 16 rounds of LOO, we used the remaining 24 subjects to train a multi-class linear SVM classifier and tested on the one test subject. For a 34 trial, the classification accuracy is averaged over all 6 actions and 16 test subjects. There exists another train/test procedure, adopted in the original experiment setup [44], that does not use LOO. Videos from 9 subjects (subjects 2, 3, 5, 6, 7, 8, 9, 10 and 22) were chosen for the test set, and videos from the remaining 16 subjects were divided evenly into training and validation sets. Compared to the protocol of [44] where the training, test and validation sets are fixed, the LOO protocol that randomizes the training/test split is less pruned to overfitting. Overfitting is highly likely since countless methods have been tried on KTH over the past 6 years. Hence, we chose to emphasize the results following the LOO protocol, but include those following the protocol of [44] for completeness. We trained a 4-layer ST-DBN using videos from the training set, using the following settings: nWx = nWy = 8,nBx = nBy = 3 for spatial pooling layers and nWt = 6,nBt = 3 for temporal pooling layers. The number of bases is 25 for the first layer and 64 for the remaining layers. Fig. 6.2 shows a subset of layer one and two of the ST-DBN filters learned from the KTH data. The image filters in the first layer were similar to the ones widely reported in the deep learning literature. Figure 6.2: Learned layer one and layer two ST-DBN filters on KTH. For the first layer (left), each square shows a 8×8 spatial filter. For the second layer (right), each row shows a temporal filter over 6 time steps. We used a nonlinear SVM with a Gaussian RBF kernel, where the parameters of the kernel were set using 5-fold cross-validation on the combined training and 35 validation set (similar to [26]). We compare the performance using outputs from each layer of the STDBN. Since video lengths vary across the data samples, and STDBN of different depth provides different degree of resolution reduction, the output representations from the STDBN differ in size. Therefore, we reduce the resolution of the output representation down to 4×5×6 via max-pooling. 6.2.2 Classification Performance Table 6.1 shows classification results for both train/test protocols. Table 6.1: Average classification accuracy results for KTH actions dataset. LOO protocol Train/test protocol of [44] Method Accuracy (%) Method Accuracy (%) 4-layer ST-DBN 90.3 ± 0.83 4-layer ST-DBN 85.2 3-layer ST-DBN 91.13±0.85 3-layer ST-DBN 86.6 2-layer ST-DBN 89.73 ± 0.18 2-layer ST-DBN 84.6 1-layer ST-DBN 85.97 ± 0.94 1-layer ST-DBN 81.4 Liu and Shah [30] 94.2 Laptev et al. [26] 91.8 Wang and Li [50] 87.8 Taylor et al. [46] 89.1 Dollár et al. [11] 81.2 Schuldt et al. [44] 71.7 For the LOO protocol, classification results for a ST-DBN with up to 4 lay- ers are shown, averaged over 4 trials. We included comparisons to three other methods—all of which used an SVM classifier2. We see that having additional (temporal and spatial) layers yields an improvement in performance, achieving a competitive accuracy of 91% (for the 3rd layer). Interestingly, a 4th layer leads to slightly worse classification accuracy. This can be attributed to excessive tempo- ral pooling on an already short video snippet (around 100 frames long). The best reported result to date, 94.2% by Liu and Shah [30], first used a bag-of-words ap- proach on cuboid feature detectors and maximal mutual information to cluster the video-words, and then boosted up performance by using hand-designed matching 2Dean et al. [8] report a classification accuracy of 81.1% under the LOO protocol. However, their result is not directly comparable to ours since they use a 1-NN classifier, instead of an SVM classifier. 36 mechanisms to incorporate global spatio-temporal information. In contrast to their work, we do not use feature detectors or matching mechanisms, and take the en- tire video as input. Wang and Li [50] cropped the region of the video containing the relevant activity and clustered difference frames. They also used a weighted sequence descriptor that accounted for the temporal order of features. For the train/test protocol of [44], we see a similar pattern in the performance of ST-DBN as the number of layers increases. Laptev et al. [26] use a 3D Harris de- tector, a HoG/HoF descriptor, and an SVM with a χ2 kernel. Taylor et al. [46] use dense sampling of space-time cubes, along with a factored RBM model and sparse coding to produce codewords that are then fed into an SVM classifier. Schuldt et al. [44] use the space-time interest points of [25] and an SVM classifier. 6.2.3 Discussion In table 6.1, the three-layered ST-DBN performs better than most methods using the less overfitting-prone, LOO protocol, and achieves comparable accuracy in the specific train/test split used by the protocol of [44]. Unlike the existing activity recognition methods, our model learns a feature representation in a completely unsupervised manner. The model incorporates no prior knowledge for the classification task apart from spatial and temporal smooth- ness. Neither has the classification pipeline (the entire process that converts raw videos into classification results) been tuned to improve the accuracy results. For example, lower error rates could be achieved by adjusting the resolution of input features to a SVM. Despite its fully unsupervised nature, our models achieves a performance within 3% to 5% of the current state-of-the-art [26, 30], and a 10% to 15% reduction in error relative to the initial methods [11, 44] applied to KTH. 37 Chapter 7 ST-DBN as a Generative Model In this chapter we present anecdotal results of video denoising and inferring miss- ing portions of frames in a video sequence using a ST-DBN. Note that same learned ST-DBN used for discrimination in chapter 6 is used for the experiments of this chapter. To the best of our knowledge, the ST-DBN is the only model capable of performing all these tasks within a single modeling framework. 7.1 Denoising We denoise test videos from the KTH dataset using a one-layer and a two-layer STDBN. For the two-layer STDBN, we first follow the procedure described in Sec. 5.2 to generate the max-pooling units of the first layer, then we use Eqs. (5.4) and (4.5) to generate high-resolution version of the data. Fig. 7.1 shows denoising results. Fig. 7.1(a) shows the test frame, Fig. 7.1(b) shows the noisy test frame, corrupted with additive Gaussian noise1, and Figs. 7.1(c) and (d) show the reconstruction with a one-layer and a two-layer ST-DBN, respec- tively. We see that the one-layer ST-DBN (Fig. 7.1(c)) denoises the image frame well. The two-layer ST-DBN (with an additional temporal pooling layer) gives slightly better background denoising. The normalized MSEs of one-layer and two- layer reconstructions are 0.1751 and 0.155 respectively. For reference, the normal- 1Each pixel is corrupted with additive mean-zero Gaussian noise with a standard deviation of s, where s is the standard deviation of all pixels in the entire (clean) video. 38 ized MSE between the clean and noisy video has value 1. We include a link to the denoised video in the supplementary material since the denoising effects are more visible over time. Note that in Fig. 7.1, local contrast normalization was reversed to visualize frames in the original image space, and image frames were downsampled by a factor of 2 from the original KTH set. Figure 7.1: Denoising results: (a) Test frame; (b) Test frame corrupted with noise; (c) Reconstruction using 1-layer ST-DBN; (d) Reconstruction with 2-layer ST-DBN. 7.2 Prediction Fig. 7.2 illustrates the capacity of the ST-DBN to reconstruct data and generate spatio-temportal predictions. The test video shows an observed sequence of gazes in frames 2-4, where the focus of attention is on portions of the frame. The bottom row of Fig. 7.2 shows the reconstructed data within the gaze window and predic- tions outside this window. The gazes are simulated by keeping a randomly sampled circular region for 39 each frame and zeroing out the rest. We use a three-layer STDBN and the same reconstruction strategy as that in Sec. 7.1. In the reconstruction, for ease of visual- ization, the region outside the gaze window are contrast enhanced to have the same contrast as the region within. Note that the blurry effect in prediction outside the gaze window is due to the loss of information incurred with max-pooling. Though max-pooling comes at a cost when inferring missing parts of frames, it is crucial for good discriminative performance. Future research must address this fundamental trade-off. The re- sults in the figure, though apparently simple, are quite remarkable. They represent an important step toward the design of attentional mechanisms for gaze planning. While gazing at the subject’s head, the model is able to infer where the legs are. This coarse resolution gist may be used to guide the placement of high resolution detectors. Figure 7.2: Top video shows an observed sequence of gazes/foci of attention (i.e., frames 2-4). Bottom video shows reconstructions within the gaze windows and predictions outside them. 40 Chapter 8 Discussion and Conclusions 8.1 Discussion There remain a few avenues for discussion and future investigation. • First, testing on larger video databases is an obvious immediate topic for further research. Our model can be parallelized easily and to a high degree, since the hidden representations are factorized. Future work will investigate expediting training and inference with Graphical Processing Units. • Interestingly, the max-pooling operation that allows feature invariance to be captured hierarchically from spatio-temporal data has an adverse effect for predicting missing parts of a video sequence due to loss of resolution. To address this issue, future work will examine how to minimize the information loss associated with max-pooling when performing inference. We conjecture that combinations of models with and without pooling might be required. • Additionally, samples from our model are produced by top-down informa- tion only. Although treating our model as a Deep Boltzmann Machine and using the corresponding sampling procedure incorporating both top-down and bottom-up information, the fact that the model is trained as a Deep Be- lief Network causes a double-counting problem in inference. Future work should investigate efficient training of our model as a Deep Boltzmann Ma- chine in order to perform sampling properly. 41 • Precautions should be taken to ensure representations are not made too com- pact with too many layers in the architecture. Model selection is an open challenge in this line of research. • Finally, we plan to build on the gaze prediction results. Our intention is to use planning to optimize the gaze locations so as to solve various recognition and verification tasks efficiently. 8.2 Conclusions In this thesis, we introduced a hierarchical distributed probabilistic model for learn- ing invariant features from spatio-temporal data. Using CRBMs as a building block, our model, the Space-Time Deep Belief Network, pools over space and time. It fulfills all the four desirable properties of a feature extractor that we reviewed in the introduction. 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