Query Specific Fusion for Large-Scale Image Retrieval
Recent image retrieval algorithms based on local features indexed by a vocabulary tree and holistic features indexed by compact hashing codes both demonstrate excellent scalability. However, their retrieval precision may vary dramatically among queries. This motivates us to investigate how to fuse the ordered retrieval sets given by multiple retrieval methods, to further enhance the retrieval precision. Thus, we propose a graph-based query specific fusion approach where multiple retrieval sets are merged and reranked by conducting a link analysis on a fused graph. The retrieval quality of an individual method is measured by the consistency of the top candidates' nearest neighborhoods. Hence, the proposed method is capable of adaptively integrating the strengths of the retrieval methods using local or holistic features for different query images without any supervision. Extensive experiments demonstrate very competitive performance on 4 public datasets, i.e., the UKbench, Corel-5K, Holidays and San Francisco Landmarks datasets.