Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval
Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to 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 lea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2017/9635348 |
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| _version_ | 1849308156562243584 |
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| author | Lijuan Duan Chongyang Zhao Jun Miao Yuanhua Qiao Xing Su |
| author_facet | Lijuan Duan Chongyang Zhao Jun Miao Yuanhua Qiao Xing Su |
| author_sort | Lijuan Duan |
| collection | DOAJ |
| description | Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to 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 ability and distinction capability. In our method, we train two different architecture’s 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. |
| format | Article |
| id | doaj-art-145ce2ca1c9c409699bb0c4158e1c577 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-145ce2ca1c9c409699bb0c4158e1c5772025-08-20T03:54:33ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322017-01-01201710.1155/2017/96353489635348Deep Hashing Based Fusing Index Method for Large-Scale Image RetrievalLijuan Duan0Chongyang Zhao1Jun Miao2Yuanhua Qiao3Xing Su4Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaSchool of Computer Science, Beijing Information Science and Technology University, Beijing 100101, ChinaCollege of Applied Science, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaHashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to 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 ability and distinction capability. In our method, we train two different architecture’s 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.http://dx.doi.org/10.1155/2017/9635348 |
| spellingShingle | Lijuan Duan Chongyang Zhao Jun Miao Yuanhua Qiao Xing Su Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval Applied Computational Intelligence and Soft Computing |
| title | Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval |
| title_full | Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval |
| title_fullStr | Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval |
| title_full_unstemmed | Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval |
| title_short | Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval |
| title_sort | deep hashing based fusing index method for large scale image retrieval |
| url | http://dx.doi.org/10.1155/2017/9635348 |
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