Abstract

Abstract: Social mediasharing websites like Flickr allow users to annotate images with free tags,which significantly contribute to the development of the web image retrievaland organization. Tag-based image search is an important method to find imagescontributed by social users in such social websites. However, how to make thetop ranked result relevant and with diversity is challenging. In this paper, wepropose a social re-ranking system for tag-based image retrieval with theconsideration of image’s relevance and diversity. We aim at re-ranking imagesaccording 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 byinter-user re-ranking. Users that have higher contribution to the given queryrank higher. Then we sequentially implement intra-user re-ranking in the rankeduser’s image set, and only the most relevant image from each user’s image setis selected. These selected images compose the final retrieved results. Webuild an inverted index structure for the social image data set to acceleratethe searching process.

Keywords: Re-ranking, Social Clues, Tag-based Image Retrieval, Social Media, Imagesearch.