Next Article in Journal
On the Statistical Properties of Multiscale Permutation Entropy: Characterization of the Estimator’s Variance
Next Article in Special Issue
Entropy in Image Analysis
Previous Article in Journal
Effects of Annealing on Microstructure and Mechanical Properties of Metastable Powder Metallurgy CoCrFeNiMo0.2 High Entropy Alloy
Previous Article in Special Issue
A q-Extension of Sigmoid Functions and the Application for Enhancement of Ultrasound Images
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Large-Scale Person Re-Identification Based on Deep Hash Learning

Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 449;
Received: 31 March 2019 / Revised: 27 April 2019 / Accepted: 28 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Entropy in Image Analysis)
PDF [1746 KB, uploaded 30 April 2019]


Person re-identification in the image processing domain has been a challenging research topic due to the influence of pedestrian posture, background, lighting, and other factors. In this paper, the method of harsh learning is applied in person re-identification, and we propose a person re-identification method based on deep hash learning. By improving the conventional method, the method proposed in this paper uses an easy-to-optimize shallow convolutional neural network to learn the inherent implicit relationship of the image and then extracts the deep features of the image. Then, a hash layer with three-step calculation is incorporated in the fully connected layer of the network. The hash function is learned and mapped into a hash code through the connection between the network layers. The generation of the hash code satisfies the requirements that minimize the error of the sum of quantization loss and Softmax regression cross-entropy loss, which achieve the end-to-end generation of hash code in the network. After obtaining the hash code through the network, the distance between the pedestrian image hash code to be retrieved and the pedestrian image hash code library is calculated to implement the person re-identification. Experiments conducted on multiple standard datasets show that our deep hashing network achieves the comparable performances and outperforms other hashing methods with large margins on Rank-1 and mAP value identification rates in pedestrian re-identification. Besides, our method is predominant in the efficiency of training and retrieval in contrast to other pedestrian re-identification algorithms. View Full-Text
Keywords: person re-identification; image analysis; hash layer; quantization loss; Hamming distance; cross-entropy loss person re-identification; image analysis; hash layer; quantization loss; Hamming distance; cross-entropy loss

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ma, X.-Q.; Yu, C.-C.; Chen, X.-X.; Zhou, L. Large-Scale Person Re-Identification Based on Deep Hash Learning. Entropy 2019, 21, 449.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top