Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 41, Issue 6, Pages 1501-1514Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2018.2833865
Keywords
Binary descriptors; unsupervised learning; deep learning; convolutional neural networks
Funding
- National Key Research and Development Program of China [2017YFA0700802]
- Ministry of Science 929 and Technology of Taiwan [MOST 105-2218-E-001-006]
- National Natural Science Foundation of China [U1713214, 61672306]
Ask authors/readers for more resources
Binary descriptors have been widely used for efficient image matching and retrieval. However, most existing binary descriptors are designed with hand-craft sampling patterns or learned with label annotation provided by datasets. In this paper, we propose a new unsupervised deep learning approach, called DeepBit, to learn compact binary descriptor for efficient visual object matching. We enforce three criteria on binary descriptors which are learned at the top layer of the deep neural network: 1) minimal quantization loss, 2) evenly distributed codes and 3) transformation invariant bit. Then, we estimate the parameters of the network through the optimization of the proposed objectives with a back-propagation technique. Extensive experimental results on various visual recognition tasks demonstrate the effectiveness of the proposed approach. We further demonstrate our proposed approach can be realized on the simplified deep neural network, and enables efficient image matching and retrieval speed with very competitive accuracies.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available