The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.
Deep hashing for compact binary codes learning
Abstract
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the deep network: 1) the loss between the original real-valued feature descriptor and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) by including one discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes. Experimental results show the superiority of the proposed approach over the state-of-the-arts.
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2 of the 40 references couldn’t be matched to a known paper.
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- taylor, 2010, Convolutionallearning of spatio-temporal features, ECCV, 140
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Deep Hashing Network for Unsupervised Domain Adaptation
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Metadata
- Authors
- Liong, V. E., Lu, J., Wang, G., Moulin, P., & Zhou, J.
- Affiliations
- Advanced Digital Sciences Center (SG)
- Nanyang Technological University (SG)
- University of Illinois Urbana-Champaign (US)
- Tsinghua University (CN)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 2475-2483
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1956333070
- Cited by
- 545
- References
- 40
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 19 · 2023: 28 · 2022: 46 · 2021: 49 · 2020: 70 · 2019: 82 · 2018: 88 · 2017: 102 · 2016: 33 · 2015: 9
- FWCI
- 31.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- deep hashing, binary code learning, compact binary codes, visual search, large-scale image retrieval
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