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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 ability and distinction capability. In our method, we train two different architecture\u2019s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.<\/jats:p>","DOI":"10.1155\/2017\/9635348","type":"journal-article","created":{"date-parts":[[2017,5,24]],"date-time":"2017-05-24T17:03:52Z","timestamp":1495645432000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval"],"prefix":"10.1155","volume":"2017","author":[{"given":"Lijuan","family":"Duan","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"National 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