Author/Authors :
Duan, Lijuan Faculty of Information Technology - Beijing University of Technology, Beijing, China , Zhao, Chongyang Faculty of Information Technology - Beijing University of Technology, Beijing, China , Miao, Jun School of Computer Science - Beijing Information Science and Technology University, Beijing, China , Qiao, Yuanhua College of Applied Science - Beijing University of Technology, Beijing, China , Su, Xing Faculty of Information Technology - Beijing University of Technology, Beijing, China
Abstract :
Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrievalto solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method,called the Deep Hashing based Fusing Index (DHFI), to generate a more compact hash code which has stronger expression abilityand distinction capability. In our method, we train two different architecture’s deep hashing sub networks and fuse the hash codes generated by the two sub networks together to unify images. Experiments on two real datasets show that our method can out perform state-of-the-art image retrieval applications
