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Learning Non-Redundant Codebooks for Classifying Complex Objects

Learning Non-Redundant Codebooks for Classifying Complex Objects

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. Codebook-based representations are widely employed in the classification of complex objects such as images and documents. Most previous codebook-based methods construct a single codebook via clustering that maps a bag of low-level features into a fixed-length histogram that describes the distribution of these features. This paper describes a simple yet effective framework for learning multiple non-redundant codebooks that produces surprisingly good results. In this framework, each codebook is learned in sequence to extract discriminative information that was not captured by preceding codebooks and their corresponding classifiers. We apply this framework to two application domains: visual object... Show More
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