Deep classification hashing for person re-identification

Jiabao Wang; Yang Li; Xiancai Zhang; Zhuang Miao; Gang Tao

doi:10.1117/12.2302474

10 April 2018 Deep classification hashing for person re-identification

Jiabao Wang, Yang Li, Xiancai Zhang, Zhuang Miao, Gang Tao

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150L (2018) https://doi.org/10.1117/12.2302474
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

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.

Citation Download Citation

Jiabao Wang, Yang Li, Xiancai Zhang, Zhuang Miao, and Gang Tao "Deep classification hashing for person re-identification", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150L (10 April 2018); https://doi.org/10.1117/12.2302474

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