Skip to main content
SpringerLink
Log in

Interactive Fuzzy Cellular Automata for Fast Person Re-Identification

  • Conference paper
  • First Online:
The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

One of the goals of person re-identification systems is to support video-surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with a given probe individual. Existing appearance descriptors, together with their similarity measures, are mostly aimed at improving ranking quality. We propose two fuzzy-based descriptors which are fast in terms of the processing time on descriptor generation and matching score computation. We then evaluate our approach on three benchmark data sets (VIPeR, i-LIDS, and ETHZ) with comparison of some descriptors in the state-of-the-art.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image Vis. Comput. 32(4), 270–286 (2014)

    Article  Google Scholar 

  2. Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: BMVC, p. 6 (2011)

    Google Scholar 

  3. Choi, Y., Krishnapuram, R.: A robust approach to image enhancement based on fuzzy logic. IEEE Trans. Image Proc. 6(6), 808–825 (1997)

    Article  Google Scholar 

  4. Dikmen, M., Akbas, E., Huang, T.S., Ahuja, N.: Pedestrian recognition with a learned metric. In: ACCV 2010, pp. 501–512. Springer, Heidelberg (2011)

    Google Scholar 

  5. Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Conference on CVPR, pp. 2360–2367. IEEE (2010)

    Google Scholar 

  6. Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: ECCV 2008, pp. 262–275. Springer, Heidelberg (2008)

    Google Scholar 

  7. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Proc. 19(6), 1657–1663 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hernandez, G., Herrmann, H.J.: Cellular automata for elementary image enhancement. Graph. Models Image Proc. 58(1), 82–89 (1996)

    Article  Google Scholar 

  9. Hirzer, M., Roth, P.M., Bischof, H.: Person re-identification by efficient impostor-based metric learning. In: AVSS, pp. 203–208. IEEE (2012)

    Google Scholar 

  10. Lavi, B., Fumera, G., Roli, F.: A multi-stage approach for fast person re-identification. In: Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, S+SSPR, pp. 63–73. Springer, Cham (2016)

    Google Scholar 

  11. Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: CVPR, pp. 2197–2206 (2015)

    Google Scholar 

  12. Liao, S., Zhao, G., Kellokumpu, V., Pietikäinen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE Conference on CVPR, pp. 1301–1306. IEEE (2010)

    Google Scholar 

  13. Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32(6), 379–390 (2014)

    Article  Google Scholar 

  14. Patel, D.K., More, S.A.: Edge detection technique by fuzzy logic and cellular learning automata using fuzzy image processing. In: 2013 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6. IEEE (2013)

    Google Scholar 

  15. Pradipta, M., Chaudhuri, P.P.: Fuzzy cellular automata for modeling pattern classifier. IEICE Trans. Inf. Syst. 88(4), 691–702 (2005)

    Google Scholar 

  16. Prosser, B., Zheng, W.S., Gong, S., Xiang, T., Mary, Q.: Person re-identification by support vector ranking. In: BMVC, p. 6 (2010)

    Google Scholar 

  17. Rosin, P.L.: Image processing using 3-state cellular automata. Comput. Vis. Image Underst. 114(7), 790–802 (2010)

    Article  Google Scholar 

  18. Russo, F., Ramponi, G.: A fuzzy filter for images by impulse noise. IEEE Signal Process. Lett. 3(6), 168–170 (1996)

    Article  Google Scholar 

  19. Sahota, P., Daemi, M., Elliman, D.: Training genetically evolving cellular automata for image processing. In: Speech, Image Processing and Neural Networks, pp. 753–756. IEEE (1994)

    Google Scholar 

  20. Satta, R., Fumera, G., Roli, F., Cristani, M., Murino, V.: A multiple component matching framework for person re-id. In: ICIAP, pp. 140–149. Springer, Heidelberg (2011)

    Google Scholar 

  21. Shahverdi, R., Tavana, M., Ebrahimnejad, A., Zahedi, K., Omranpour, H.: An improved method for edge detection and image segmentation using fuzzy cellular automata. Cybern. Syst. 47(3), 161–179 (2016)

    Article  Google Scholar 

  22. Sompong, C., Wongthanavasu, S.: An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm. Expert Syst. Appl. 72, 231–244 (2017)

    Article  Google Scholar 

  23. Tyan, C.Y., Wang, P.P.: Image processing-enhancement, filtering and edge detection using the fuzzy logic approach. In: Second IEEE International Conference on Fuzzy Systems, pp. 600–605. IEEE (1993)

    Google Scholar 

  24. Zheng, W.S., Gong, S., Xiang, T.: Associating groups of people. In: BMVC, vol. 2, p. 6 (2009)

    Google Scholar 

  25. Zheng, W.S., Gong, S., Xiang, T.: Reidentification by relative distance comparison. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 653–668 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Bahram Lavi & Muhammad Atta Othman Ahmed

Authors
  1. Bahram Lavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Atta Othman Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bahram Lavi .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lavi, B., Ahmed, M.A.O. (2018). Interactive Fuzzy Cellular Automata for Fast Person Re-Identification. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74690-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation