Category sensitive codebook construction for object category recognition

Recently, the bag of visual words based image representation is getting popular in object category recognition. Since the codebook of the bag-of-words (BOW) based image representation approach is typically constructed by only measuring the visual similarity of local image features (e.g., k-means), the resulting codebooks may not capture the desired information for object category recognition. This paper proposes a novel optimization method for discriminative codebook construction that considers the category information of local image features as an additional term in traditional visual-similarity-only based codebook construction methods. The category sensitive codebook is constructed through solving an optimization problem. Therefore, the category sensitive codebook construction method goes one step beyond visual-similarity-only methods. Besides, the proposed category sensitive codebook construction method can be implemented with k-means clustering very efficiently and effectively. Experimental results on PASCAL VOC Challenge 2006 data set demonstrate the effectiveness of our method.