Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks

Kevin Lin, Jiwen Lu, Chu-Song Chen, Jie Zhou

CVPR 2016

doi:10.1109/CVPR.2016.133 /cvpr/2016/lin2016cvpr-learning/

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 a 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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Cite

Text

Lin et al. "Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.133

Markdown

[Lin et al. "Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/lin2016cvpr-learning/) doi:10.1109/CVPR.2016.133

BibTeX

@inproceedings{lin2016cvpr-learning,
  title     = {{Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks}},
  author    = {Lin, Kevin and Lu, Jiwen and Chen, Chu-Song and Zhou, Jie},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.133},
  url       = {https://mlanthology.org/cvpr/2016/lin2016cvpr-learning/}
}