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UBC Theses and Dissertations

Object recognition with many local features Helmer, Scott

Abstract

There has been a great deal of attention focused on part-based approaches to object classification in recent research in computer vision, and some approaches have achieved a surprising amount of success. However, learning models with a large number of parts has been a particular challenge. One of the most successful approaches is that of Fergus et al. [5] who have developed a generative model for recognition that achieves excellent results on a variety of datasets. The learning method that they present to learn the parameters for the model, however, requires an exponential amount of time to train as the number of parts increase. The primary contribution of this thesis is the extension of their generative model, and the development of a learning algorithm that can learn a large number of parts in a reasonable amount of time. In particular, we have developed an incremental learning algorithm where the model is initialized intelligently with a small number of parts, and parts are added to the model one at a time. By taking such an approach we are able to learn models with a large number of parts in nearly a linear amount of time in the number of parts. The approach is validated on a number of datasets, including cars, motorbikes, and faces, and demonstrates excellent recognition results along with large models learned in a reasonable amount of time.

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