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Proceedings Paper

Deep classification hashing for person re-identification
Author(s): Jiabao Wang; Yang Li; Xiancai Zhang; Zhuang Miao; Gang Tao
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Paper Abstract

As the development of surveillance in public, person re-identification becomes more and more important. The largescale databases call for efficient computation and storage, hashing technique is one of the most important methods. In this paper, we proposed a new deep classification hashing network by introducing a new binary appropriation layer in the traditional ImageNet pre-trained CNN models. It outputs binary appropriate features, which can be easily quantized into binary hash-codes for hamming similarity comparison. Experiments show that our deep hashing method can outperform the state-of-the-art methods on the public CUHK03 and Market1501 datasets.

Paper Details

Date Published: 10 April 2018
PDF: 5 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150L (10 April 2018); doi: 10.1117/12.2302474
Show Author Affiliations
Jiabao Wang, PLA Army Engineering Univ. (China)
Yang Li, PLA Army Engineering Univ. (China)
Xiancai Zhang, PLA Army Engineering Univ. (China)
Zhuang Miao, PLA Army Engineering Univ. (China)
Gang Tao, Anhui Keli Information Industry Co. Ltd. (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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