Simultaneous feature learning and hash coding with deep neural networks
Open accessAbstract
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.
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The performance of hashing schemes may degrade significantly if the projected dimensions are imbalanced, i.e., their variances vary a lot.
“Unfortunately, it has been acknowledged that the performance of those hashing schemes may degrade significantly if the projected dimensions are imbalanced [4], [6], [13], i.e., their variances vary a lot.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [13]
The Introduction (p. 1) discusses features that may be incompatible with hash coding, while Section 3.3 (p. 4) discusses redundancy among hash bits. Neither addresses whether unequal variances across projected dimensions significantly degrade hashing performance (searched: projected dimensions, imbalanced, variance). (LLM, conf=0.74)
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- Authors
- Lai, H., Pan, Y., Liu, Y., & Yan, S.
- Affiliations
- National University of Singapore (SG)
- Sun Yat-sen University (CN)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3270-3278
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1939575207
- Cited by
- 884
- References
- 28
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 34 · 2023: 69 · 2022: 70 · 2021: 103 · 2020: 116 · 2019: 152 · 2018: 128 · 2017: 123 · 2016: 41 · 2015: 11
- FWCI
- 41.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- supervised hashing, binary codes, image retrieval, nearest neighbor search, representation learning, convolutional neural networks, similarity-preserving hashing
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