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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...
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...
Recently, a large number of geotagged images are available on photo sharing Web sites such as Flickr. In this paper, we propose image region entropy and geo-location entropy for analyzing the relation between visual concepts and geographical locations using a large-scale geotagged image database. Image region entropy represents to what extent concepts have visual characteristics, while geo-location...
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