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In this paper, we present a novel approach for supervised codebook learning and optimization for bag-ofwords models. This type of models is frequently used in visual recognition tasks like object class recognition or human action recognition. An entity is represented as a histogram of codewords, which are traditionally clustered with unsupervised methods like k-means or random forests and then classified in a supervised way. We propose a new supervised method for joint codebook creation anddoi:10.1007/s12559-012-9137-4 fatcat:l5mfsv7uxraj5lbdgtvqi5adsy