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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 7
2227-2231

Improved KISSME method for person re-identification based on normality resampling

Song Lili1
Li Bin2
Zhao Junya3
Liu Guofeng3
1. College of Engineering & Technical, Chengdu University of Technology, Leshan Sichuan 614000, China
2. School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China
3. School of Science, Wuhan University of Technology, Wuhan 430070, China
DOI: 10.19734/j.issn.1001-3695.2019.02.0098

Abstract

As two critically important parts of the cross-camera pedestrian re-recognition method, feature recognition and metric model establishment have been constrained by notorious overfitting of model parameter estimation that arises from improper image sample distribution. This study proposed an improved, semi-supervised KISSME learning algorithm-based method for pedestrian re-recognition. The proposed method succeeded to construct a generalized measurement model by re-sampling normally distributed data, weakening fitting strength through establishment of a circular optimization metric learning method, and improving the model's generalization capacity. Then, it introduced KISSME metrics to further improve the semi-supervised model. Finally, it verified effectiveness of the improved algorithm by pedestrian re-recognition using the public open VIPeR dataset, results of which were compared with accuracies of SLDDL, RDC, ITML, PCCA, QARR-RSVM and KISSME. It demonstrated the improved, semi-supervised KISSME algorithm to be superior in all recognition accuracy ranks, especially in the rank-1. It achieved an accuracy that was 3.14% higher than that of the existing KISSME algorithm, thereby validating effectiveness of the algorithm proposed in this study.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.02.0098
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Technology of Graphic & Image
Pages: 2227-2231
Serial Number: 1001-3695(2020)07-064-2227-05

Publish History

[2020-07-05] Printed Article

Cite This Article

宋丽丽, 李彬, 赵俊雅, 等. 基于样本正态性重采样的改进KISSME行人再识别算法 [J]. 计算机应用研究, 2020, 37 (7): 2227-2231. (Song Lili, Li Bin, Zhao Junya, et al. Improved KISSME method for person re-identification based on normality resampling [J]. Application Research of Computers, 2020, 37 (7): 2227-2231. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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