Learning Non-Redundant Codebooks for Classifying Complex Objects
published: Aug. 26, 2009, recorded: June 2009, views: 3755
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !