An Effective and Efficient Re-ranking Framework for Social Image Search

With the rapidly increasing popularity of social media websites, large numbers of images with user-annotated tags are uploaded by web users. Developing automatic techniques to retrieval such massive social images attracts much attention of researchers. The method of social image search returns top-k images according to several keywords input by users. However, the returned results by existing methods are usually irrelevant or lack of diversity, which cannot satisfy user’s veritable intention. In this paper, we propose an effective and efficient re-ranking framework for social image search, which can quickly and accurately return ranking results. We not only consider the consistency of visual content of images and semantic interpretations of tags, but also maximize the coverage of the user’s query demand. Specifically, we first build a social relationship graph by exploring the heterogeneous attribute information of social networks. For a given query, to ensure the effectiveness, we execute an efficient keyword search algorithm over the social relationship graph, and obtain top-k relevant candidate results. Moreover, we propose a novel re-ranking optimization strategy to refine the candidate results. Meanwhile, we develop an index to accelerate the optimization process, which ensures the efficiency of our framework. Extensive experimental conducts on real-world datasets demonstrate the effectiveness and efficiency of proposed re-ranking framework.