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Information Processing and Management: an International Journal
C4.5: programs for machine learning
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Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
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Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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A divisive information theoretic feature clustering algorithm for text classification
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Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Simultaneous Unsupervised Learning of Disparate Clusterings
Statistical Analysis and Data Mining
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Supervised learning of Gaussian mixture models for visual vocabulary generation
Pattern Recognition
A Review of Codebook Models in Patch-Based Visual Object Recognition
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Online semi-supervised discriminative dictionary learning for sparse representation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Discriminative dictionary learning with pairwise constraints
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Multiple kernel learning for emotion recognition in the wild
Proceedings of the 15th ACM on International conference on multimodal interaction
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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.