A graphic-theoretic model for incremental relevance feedback in image retrieval

Many traditional relevance feedback approaches for content-based image retrieval (CBIR) can only achieve limited short-term performance improvement without benefiting long-term performance. To remedy this limitation, we propose a graphic-theoretic model for incremental relevance feedback in image retrieval. Firstly, a two-layered graph model is introduced that describes the correlations between images. A teaming strategy is then suggested to enrich the graph model with semantic correlations between images derived from user feedback. Based on the graph model, we propose a link analysis approach for image retrieval and relevance feedback. Experiments conducted on real-world images have demonstrated the advantage of our approach over traditional approaches in both short-term and long-term performance.