The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
Abstract
In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching. Unlike most existing binary descriptors which were designed with random projections or linear hash functions, we develop a deep neural network to learn binary descriptors in an unsupervised manner. We enforce three criterions on binary codes which are learned at the top layer of our network: 1) minimal loss quantization, 2) evenly distributed codes and 3) uncorrelated bits. Then, we learn the parameters of the networks with a back-propagation technique. Experimental results on three different visual analysis tasks including image matching, image retrieval, and object recognition clearly demonstrate the effectiveness of the proposed approach.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Histograms of Oriented Gradients for Human Detection
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
Robust Real-Time Face Detection
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Cited by
A Survey on Deep Hashing Methods
Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241
Self-citationHPatches: A benchmark and evaluation of handcrafted and learned local descriptors
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Wang, K., Ma, S., Chen, J., Ren, F., & Lu, J. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3038898 · Cited by 114
“To learn compact binary descriptors, an unsupervised-based method was introduced in [69], which enforces the criterion of minimal loss quantization, uniformly distributed code, and uncorrelated bits.”
MethodSelf-citation(2018) · Cited by 22
Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval
Zhu, D., Song, D., Chen, Y., Lumezanu, C., Cheng, W., Zong, B., Ni, J., Mizoguchi, T., Yang, T., & Chen, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5497 · Cited by 20
Metadata
- Authors
- Lin, K., Lu, J., Chen, C.-S., & Zhou, J.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1183-1192
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2461086877
- Cited by
- 363
- References
- 55
- FWCI
- 17.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lin, K., Lu, J., Chen, C.-S., & Zhou, J. (2016). Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.133
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