Re-ranking by Multi-modal Relevance Feedback for Content-Based Social Image Retrieval

Abstract

With the recent rapid growth of social image hosting websites, it is becoming increasingly easy to construct a large database of tagged images. In this paper, we investigate whether and how social tags can be used for improving content-based image search results, which has not been well investigated in existing work. We propose a multi-modal relevance feedback scheme and a supervised re-ranking approach by using social tags. Our multi-modal scheme utilizes both image and social tag relevance feedback instances. The approach propagates visual and textual information and multi-modal relevance feedback information on an image-tag relationship graph with a mutual reinforcement process. We conduct experiments showing that our approach can successfully use social tags in the re-ranking of content-based social image search results and perform better than other approaches. Additional experiment shows that our multi-modal relevance feedback scheme significantly improves performance compared with the traditional single-modal scheme.