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Proceedings Paper

Rotation invariant deep binary hashing for fast image retrieval
Author(s): Lai Dai; Jianming Liu; Aiwen Jiang
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Paper Abstract

In this paper, we study how to compactly represent image’s characteristics for fast image retrieval. We propose supervised rotation invariant compact discriminative binary descriptors through combining convolutional neural network with hashing. In the proposed network, binary codes are learned by employing a hidden layer for representing latent concepts that dominate on class labels. A loss function is proposed to minimize the difference between binary descriptors that describe reference image and the rotated one. Compared with some other supervised methods, the proposed network doesn’t have to require pair-wised inputs for binary code learning. Experimental results show that our method is effective and achieves state-of-the-art results on the CIFAR-10 and MNIST datasets.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201Z (21 July 2017); doi: 10.1117/12.2281692
Show Author Affiliations
Lai Dai, Jiangxi Normal Univ. (China)
Jianming Liu, Jiangxi Normal Univ. (China)
Aiwen Jiang, Jiangxi Normal Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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