TAG BASED IMAGE SEARCH BY SOCIAL RE-RANKING IN THE WEB BASED APPLICATIONS
Keywords:Re-ranking, image set, data set.
Social media sharing websites like Flickr allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval and organization. Tag-based image search is an important method to find images contributed by social users in such social websites. However, how to make the top ranked result relevant and with diversity is challenging. Proposing a social re-ranking system for tag-based image retrieval with the consideration of images relevance and diversity is done. Proposed system aims at re-ranking images according to their visual information, semantic information and
social clues. The initial results include images contributed by different social users. Usually each user contributes several images. First sorting the images by inter-user re-ranking is done. Users that have higher contribution to the given query rank higher. Then sequentially implementing intra-user re-ranking on the ranked users image set, and only the most relevant image from each users image set is selected. These selected images compose the final retrieved results thus building an inverted index structure for the social image dataset to accelerate the searching process. Experimental results on Flickr dataset show that our social re-ranking method is effective and efficient in the real world.
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