Dictionary generation is a core technique of the bag-of- visual-words (BOV) models when applied to image cate- gorization. Most of previous approaches generate dictio- naries by unsupervised clustering techniques, e.g. k-means. However, the features obtained by such kind of dictionaries may not be optimal for image classification. In this paper, we propose a probabilistic model for supervised dictionary learning (SDLM) which seamlessly combines an unsuper- vised model (a Gaussian Mixture Model) and a supervised model (a logistic regression model) in a probabilistic frame- work. In the model, image category information directly affects the generation of a dictionary. A dictionary ob- tained by this approach is a trade-off between minimization of distortions of clusters and maximization of discriminative power of image-wise representations, i.e. histogram repre- sentations of images. We further extend the model to incor- porate spatial information during the dictionary learning process in a spatial pyramid matching like manner. We ex- tensively evaluated the two models on various benchmark dataset and obtained promising results.<\/p>\n","protected":false},"excerpt":{"rendered":"
Dictionary generation is a core technique of the bag-of- visual-words (BOV) models when applied to image cate- gorization. Most of previous approaches generate dictio- naries by unsupervised clustering techniques, e.g. k-means. However, the features obtained by such kind of dictionaries may not be optimal for image classification. In this paper, we propose a probabilistic model […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"chw"},{"type":"text","value":"Bao-Liang Lu"},{"type":"user_nicename","value":"leizhang"},{"type":"text","value":"Xiao-Chen Lian"},{"type":"user_nicename","value":"zli"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"CVPR '10. 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