Rank-aware graph fusion with contextual dissimilarity measurement for image retrieval

In content based image retrieval, due to the diverse variations of visual content, the retrieval performance from single feature or retrieval method is usually limited. Generally, better retrieval results are obtained by combining multiple visual features. In this work, we propose a rank-aware graph fusion scheme to fuse the results from multiple retrieval methods. We first refine the initial ranking result by enhancing the neighbor reversibility of database images. Then, we adopt a graph structure to represent the retrieval results and embed the rank-prior of images to discriminate edge weight in the graph. Finally, the new relevance scores of images are deduced to re-rank images. Evaluation on two public datasets demonstrates the effectiveness of our approach.