Search

In this paper, we address the problem of object retrieval by hyperlinking the reference data set at subimage level. One of the main challenges in object retrieval involves small objects on cluttered backgrounds, where the similarity between the querying object and a relevant image can be heavily affected by the background. To address this problem, we propose an efficient object retrieval technique by hyperlinking the visual entities among the reference data set. In particular, a two-step framework is proposed: subimage-level hyperlinking and hyperlink-aware reranking. For hyperlinking, we propose a scalable object mining technique using Thread-of-Features, which is designed for mining subimage-level objects. For reranking, the initial search results are reranked with a hyperlink-aware transition matrix encoding subimage-level connectivity. Through this framework, small objects can be retrieved effectively. Moreover, our method introduces only a tiny computation overhead to online processing, due to the sparse transition matrix. The proposed technique is featured by the novel perspective (object hyperlinking) for visual search, as well as the object hyperlinking technique. We demonstrate the effectiveness and efficiency of our hyperlinking and retrieval methods by experimenting upon several object-retrieval data sets.