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In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text...
Image category recognition is important to access visual information on the level of objects and scene types. This paper combines different feature representations of images and learn a compact subspace of different features for the automatic recognition of object and scene classes. Compact visual-words and low-level-features object class subspaces are automatically learned from a set of training...
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation "group" between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories...
With the explosive growth of Web resources, how to mine semantically relevant images efficiently becomes a challenging and necessary task. In this paper, we propose a concept sensitive Markov stationary feature (C-MSF) to represent images and also present a classifier based scheme for web image mining. First, through analyzing the results of Google Image Searcher, we collect an image set, which are...
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