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Multiscale conditional random fields for machine vision Duvenaud, David
Abstract
We develop a single joint model which can classify images and label super-pixels, based on tree-structured conditional random fields (CRFs) derived from a hierarchical image segmentation, extending previous work by Reynolds and Murphy, and Plath and Toussaint. We show how to train this model in a weakly-supervised fashion, in which some of the images only have captions specifying which objects are present; this information is propagated down the tree and thus provides weakly labeled data at the leaves, which can be used to improve the performance of the super-pixel classifiers. After training, information can be propagated from the super-pixels up to the root-level image classifier (although this does not seem to help in practice compared to just using root-level features). We compare two kinds of tree: the standard one with pairwise potentials, and one based on noisy-or potentials, which better matches the semantics of the recursive partitioning used to create the tree. However, we do not find any significant difference between the two.
Item Metadata
Title |
Multiscale conditional random fields for machine vision
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2010
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Description |
We develop a single joint model which can classify images and label super-pixels, based on tree-structured conditional random fields (CRFs) derived from a hierarchical image segmentation, extending previous work by Reynolds and Murphy, and Plath and Toussaint. We show how to train this model in a weakly-supervised fashion, in which some of the images only have captions specifying which objects are present; this information is propagated down the tree and thus provides weakly labeled data at the leaves, which can be used to improve the performance of the super-pixel classifiers. After training, information can be propagated from the super-pixels up to the root-level image classifier (although this does not seem to help in practice compared to just using root-level features). We compare two kinds of tree: the standard one with pairwise potentials, and one based on noisy-or potentials, which better matches the semantics of the recursive partitioning used to create the tree. However, we do not find any significant difference between the two.
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Genre | |
Type | |
Language |
eng
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Date Available |
2010-07-30
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial 3.0 Unported
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DOI |
10.14288/1.0051919
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2010-11
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial 3.0 Unported