Soft Computing

, Volume 21, Issue 19, pp 5729–5739 | Cite as

Adaptive image segmentation based on color clustering for person re-identification

  • Lixia Zhang
  • Kangshun Li
  • Yan Zhang
  • Yu Qi
  • Lei Yang
Methodologies and Application

Abstract

Person re-identification refers to identifying a particular person who has occurred in the monitoring network automatically by computer in the surveillance video, which is significantly important for the improvement of intelligence of video monitoring. But the research of person re-identification is not mature and facing many challenges. The following factors may lead to a certain difference for the same person in different monitoring video images; for example: the illumination changing in the monitoring environment, the shooting angle difference, and the posture difference. These may lead to low recognition accuracy. In this paper, a new appearance-based person re-identification method was proposed and an investigation was launched on the following topics for the improvement of recognition accuracy. First, a simple and feasible method for color invariants was proposed, so that the affection of color by change of illumination and shooting angle could be eliminated. Then, a highly adaptive image segmentation method based on color clustering and a color feature representation scheme for specific color characteristics were designed, which could help to extract color features in more reasonable points. Finally, an effective similarity measure criterion was obtained through QSF measure learning, which could ensure that the different pedestrians can be distinguished and was better able to capture the visual change of the same person. In addition, the traditional evolutionary algorithm was improved and applied to the process of iterative computation for QSF. The experimental results show that our method is an effective way for the person re-identification problem.

Keywords

Person re-identification Adaptive image segmentation  Color invariants Color clustering Evolutionary algorithm 

Notes

Acknowledgments

This work was jointly supported by the Natural Science Foundation of Guangdong Province of China (#2015A030313408), and Natural Science Foundation of China (#61573157).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Lixia Zhang
    • 1
  • Kangshun Li
    • 1
  • Yan Zhang
    • 1
  • Yu Qi
    • 1
  • Lei Yang
    • 1
  1. 1.College of Mathematics and InformaticsSouth China Agricultural UniversityGuangzhouChina

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