Tag-Based Image Search by Social Re-ranking

Abstract

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. In this paper, we propose a social re-ranking system for tag-based image retrieval with the consideration of an image's relevance and diversity. We aim 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, we sort these images by inter-user re-ranking. Users that have higher contribution to the given query rank higher. Then we sequentially implement intra-user re-ranking on the ranked user's image set, and only the most relevant image from each user's image set is selected. These selected images compose the final retrieved results. We build an inverted index structure for the social image dataset to accelerate the searching process. Experimental results on a Flickr dataset show that our social re-ranking method is effective and efficient.