Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval. (24th May 2017)
- Record Type:
- Journal Article
- Title:
- Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval. (24th May 2017)
- Main Title:
- Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval
- Authors:
- Duan, Lijuan
Zhao, Chongyang
Miao, Jun
Qiao, Yuanhua
Su, Xing - Other Names:
- Ejbali Ridha Academic Editor.
- Abstract:
- Abstract : 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.
- Is Part Of:
- Applied computational intelligence and soft computing. Volume 2017(2017)
- Journal:
- Applied computational intelligence and soft computing
- Issue:
- Volume 2017(2017)
- Issue Display:
- Volume 2017, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 2017
- Issue:
- 2017
- Issue Sort Value:
- 2017-2017-2017-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-05-24
- Subjects:
- Computational intelligence -- Periodicals
Soft computing -- Periodicals
006.305 - Journal URLs:
- https://www.hindawi.com/journals/acisc/ ↗
- DOI:
- 10.1155/2017/9635348 ↗
- Languages:
- English
- ISSNs:
- 1687-9724
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22899.xml