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Deep Discrete Supervised Hashing
- Source :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 27(12)
- Publication Year :
- 2018
-
Abstract
- Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.
- Subjects :
- FOS: Computer and information sciences
business.industry
Computer science
Hash function
02 engineering and technology
010501 environmental sciences
Machine learning
computer.software_genre
01 natural sciences
Computer Graphics and Computer-Aided Design
Computer Science - Information Retrieval
Data_FILES
0202 electrical engineering, electronic engineering, information engineering
Key (cryptography)
020201 artificial intelligence & image processing
Artificial intelligence
business
Feature learning
Image retrieval
computer
Information Retrieval (cs.IR)
Software
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 19410042
- Volume :
- 27
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Accession number :
- edsair.doi.dedup.....750bcf3aec3e93aca4179682481eed8a