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


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.


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



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.


  1. Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using spatial covariance regions of human body parts. In: IEEE Int’l conf. advanced video and signal based surveillance, pp 435–440Google Scholar
  2. Baltieri D, Vezzani R, Cucchiara R (2014) Mapping appearance descriptors on 3d body models for people re-identification. Int J Comput Vis 111(3):345–364Google Scholar
  3. Bazzani L, Cristani M, Perina A, Murino V (2012) Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recognit Lett 33:898–903CrossRefGoogle Scholar
  4. Bazzani L, Cristani M, Murino V (2013) Symmetry-driven accumulation of local features for human characterization and re-identification. Comput Vis Image Underst 117:130–144CrossRefzbMATHGoogle Scholar
  5. Bedagkar GA, Shishir SK (2012) Part-based spatio-temporal model for multi-person re-identification. Pattern Recognit Lett 33:1908–1915CrossRefGoogle Scholar
  6. Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. In: Intl. Conf. on image analysis and recognition, vol 10, pp 191–203Google Scholar
  7. Chang DX, Zhao Y, Zheng CW, Zhang XD (2012) Genetic clustering algorithm using a message-based similarity measure. Expert Syst Appl 39:2194–2202CrossRefGoogle Scholar
  8. Cheng DS, Cristani M, Stoppa M (2011) Custom pictorial structures for re-identification. In: BMVC2011, vol 2, pp 1–6Google Scholar
  9. Chu J, Miao J, Zhang GM, Wang L (2013) Edge and corner detection by color invariants. Opt Laser Technol 45:756–762CrossRefGoogle Scholar
  10. Cuevas E, Echavarria A, Zaldivar D (2013) A novel evolutionary algorithm inspired by the states of matter for template matching. Expert Syst Appl 40:6359–6373CrossRefGoogle Scholar
  11. Du YN, Ai HZ (2014) A statistical inference approach for person re-identification. J Electron Inf Technol 36(7):1612–1618Google Scholar
  12. Fan CX, Zhu H, Lin GF, Luo L (2013) Person re-identification based on multi-features. J Image Graph 18:711–717Google Scholar
  13. Farenzena M, Bazzani L, Perina A, Murino M, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: IEEE conference computer vision and pattern recognition, pp 2360–2367Google Scholar
  14. Funt BV, Finlayson GD (1995) Color constancy color indexing. In: IEEE trans on pattern analysis and machine intelligence, vol 17, pp 522–529Google Scholar
  15. Garcia J, Patricio MA, Berlanga A (2011) Fuzzy region assignment for visual tracking. Soft Comput 15:1845–1864CrossRefGoogle Scholar
  16. Gevers T, Smeulders A (1999) Color based object recognition. Pattern Reeognit 32:453–464CrossRefGoogle Scholar
  17. Graham DF, Bernt S, James LC (1998) Comprehensive colour image normalization. In: Proceedings of the 5th European conference on computer vision, vol 1, pp 475–490Google Scholar
  18. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensamble of localized features. In: ECCV2008, pp 262–275Google Scholar
  19. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International workshop on performance evaluation for tracking and surveillance, vol 3Google Scholar
  20. Hahnel M, Klunder D, Kraiss KF (2004) Color and texture features for person recognition. In: Neural networks 2004 proceedings, vol 1Google Scholar
  21. Harandi M, Salzmann M, Porikli F (2014) Regman divergences for infinite dimensional covariance matrice. In: IEEE conference on computer vision and pattern recognitionGoogle Scholar
  22. Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. Appl Stat, pp 100–108Google Scholar
  23. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with application to biology. In: Control and arterial intelligenceGoogle Scholar
  24. Joachims T (2002) Optimizing search engines using click through data. In: Proceedings of the eighth ACM SIGKDD international conference, pp 133–142Google Scholar
  25. Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 32:241–254CrossRefzbMATHGoogle Scholar
  26. Kviatkovsky I, Adam A, Rivlin E (2013) Color invariants for person reidentification. IEEE Trans Pattern Anal Mach Intell 35:1622–1634CrossRefGoogle Scholar
  27. Li J, Li JW, Chen XF, Jia CF, Lou WJ (2015) Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans Comput 64:425–437MathSciNetCrossRefzbMATHGoogle Scholar
  28. Maesschalck R, Jouan-Rimbaud D, Massart D (2000) The mahalanobis distance. Chemom Intell Lab Syst 50:1–18CrossRefGoogle Scholar
  29. Prosser B, Zheng WS, Gong S, Xiang T (2010) Person re-identification by support vector ranking. In: Proc. British machine vision conf. vol 1, pp 1–5Google Scholar
  30. Venkatesan S, Madane SR (2012) Face recognition system with genetic algorithm and ant colony optimization. Int J Innov Manag Technol 1:469–471Google Scholar
  31. Wang X, Doretto G,  SebastianT, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: Proc. IEEE Int’l Conf. computer vision, pp 1–8Google Scholar
  32. Weijer J, Sehlnid C (2006) Blur robust and color constant image description. In: Proc. of Int. Conf. on image processing, pp 993–996Google Scholar
  33. Yao B, Hagras H, Alhaddad MJ (2015) A fuzzy logic-based system for the automation of human behavior recognition using machine vision in intelligent environments. Soft Comput 19:499–506CrossRefGoogle Scholar
  34. Zheng WS, Gong S, Xiang T (2013) Reidentification by relative distance comparision. In: IEEE tansaction on pattern analysis and machine intelligenceGoogle Scholar
  35. Zhou H, Chen Y, Feng R (2013) A novel background subtraction method based on color invariants. Comput Vis Image Underst 117:1589–1597CrossRefGoogle Scholar
  36. Zhu W, Liang S, Wei Y (2014) Saliency optimization from robust background detection. In: IEEE conference on computer vision and pattern recognition, pp 2814–2821Google Scholar

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

Personalised recommendations