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ICML
2009
IEEE

Learning non-redundant codebooks for classifying complex objects

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Learning non-redundant codebooks for classifying complex objects
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 lowlevel 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 categorization and document classification. Experiments on large classification tasks show substantial improvements in performance compared to a single codebook or codebooks learned in a bagging style.
Wei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Di
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Wei Zhang, Akshat Surve, Xiaoli Fern, Thomas G. Dietterich
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