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Possibilistic Classifier Combination for Person Re-identification

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Pattern Recognition and Artificial Intelligence (MedPRAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1322))

Abstract

Possibility theory is particularly efficient in combining multiple information sources providing incomplete, imprecise, and conflictive knowledge. In this work, we focus on the improvement of the accuracy rate of a person re-identification system by combining multiple Deep learning classifiers based on global and local representations. In addition to the original image, we explicitly leverages background subtracted image, middle and down body parts to alleviate the pose and background variations. The proposed combination approach takes place in the framework of possibility theory, since it enables us to deal with imprecision and uncertainty factor which can be presented in the predictions of poor classifiers. This combination method can take advantage of the complementary information given by each classifier, even the weak ones. Experimental results on Market1501 publicly available dataset confirm that the proposed combination method is interesting as it can easily be generalized to different deep learning re-identification architectures and it improves the results with respect to individual classifiers.

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References

  1. Albardan, M., Klein, J., Colot, O.: SPOCC: scalable possibilistic classifier combination-toward robust aggregation of classifiers. Expert Syst. Appl. 150, 113332 (2020)

    Google Scholar 

  2. Anderson, R., Koh, Y.S., Dobbie, G.: CPF: concept profiling framework for recurring drifts in data streams. In: Kang, B.H., Bai, Q. (eds.) AI 2016. LNCS (LNAI), vol. 9992, pp. 203–214. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50127-7_17

    Chapter  Google Scholar 

  3. Baati, K., Hamdani, T.M., Alimi, A.M., Abraham, A.: A new possibilistic classifier for mixed categorical and numerical data based on a bi-module possibilistic estimation and the generalized minimum-based algorithm. Intell. Fuzzy Syst. 36(4), 3513–3523 (2019)

    Article  Google Scholar 

  4. Bouchon-Meunier, B., Dubois, D., Godo, L., Prade, H.: Fuzzy sets and possibility theory in approximate and plausible reasoning. In: Bezdek, J.C., Dubois, D., Prade, H. (eds.) Fuzzy Sets in Approximate Reasoning and Information Systems, pp. 15–190. Springer, Boston (1999). https://doi.org/10.1007/978-1-4615-5243-7_2

  5. Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: Possibilistic classifiers for numerical data. Soft Comput. 17(5), 733–751 (2013)

    Article  Google Scholar 

  6. Cho, Y.J., Yoon, K.J.: Improving person re-identification via pose-aware multi-shot matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1354–1362 (2016)

    Google Scholar 

  7. Dubois, D., Foulloy, L., Mauris, G., Prade, H.: Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Comput. 10(4), 273–297 (2004)

    Article  MathSciNet  Google Scholar 

  8. Dubois, D., Prade, H.: On several representations of an uncertain body of evidence. In: Fuzzy Information and Decision Processes, pp. 167–181 (1982)

    Google Scholar 

  9. Farahbod, F., Eftekhari, M.: Comparison of different t-norm operators in classification problems. Fuzzy Logic Syst. 2(3) (2012)

    Google Scholar 

  10. Ghorbel, M., Ammar, S., Kessentini, Y., Jmaiel, M.: Improving person re-identification by background subtraction using two-stream convolutional networks. In: Karray, F., Campilho, A., Yu, A. (eds.) ICIAR 2019. LNCS, vol. 11662, pp. 345–356. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27202-9_31

    Chapter  Google Scholar 

  11. Giannakopoulos, T., Pikrakis, A.: Audio classification. In: Introduction to Audio Analysis, Chapter 5, pp. 107–151. Academic Press (2014)

    Google Scholar 

  12. Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.): Person Re-Identification. ACVPR. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6296-4

    Book  Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  14. Hu, X., Jiang, Z., Guo, X., Zhou, Y.: Person re-identification by deep learning muti-part information complementary. In: IEEE International Conference on Image Processing (ICIP), pp. 848–852. IEEE (2018)

    Google Scholar 

  15. Huang, H., Li, D., Zhang, Z., Chen, X., Huang, K.: Adversarially occluded samples for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5098–5107 (2018)

    Google Scholar 

  16. Huang, Y., Zha, Z.J., Fu, X., Zhang, W.: Illumination-invariant person re-identification. In: ACM International Conference on Multimedia, pp. 365–373 (2019)

    Google Scholar 

  17. Huang, Z., et al.: Contribution-based multi-stream feature distance fusion method with k-distribution re-ranking for person re-identification. IEEE Access 7, 35631–35644 (2019)

    Article  Google Scholar 

  18. Karanam, S., Li, Y., Radke, R.J.: Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: IEEE International Conference on Computer Vision, pp. 4516–4524 (2015)

    Google Scholar 

  19. Li, D., Chen, X., Zhang, Z., Huang, K.: Learning deep context-aware features over body and latent parts for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 384–393 (2017)

    Google Scholar 

  20. Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 152–159 (2014)

    Google Scholar 

  21. Mansouri, N., Ammar, S., Kessentini, Y.: Improving person re-identification by combining Siamese convolutional neural network and re-ranking process. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2019)

    Google Scholar 

  22. Mercier, D., Elouedi, Z., Lefevre, E.: Sur l’affaiblissement d’une fonction de croyance par une matrice de confusion. Rencontres Francophones sur la Logique Floue et Ses Applications, pp. 277–283 (2010)

    Google Scholar 

  23. Mercier, D., Quost, B., Denœux, T.: Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inf. Fusion 9(2), 246–258 (2008)

    Article  Google Scholar 

  24. Meyer-Baese, A., Schmid, V.: Foundations of neural networks. In: Pattern Recognition and Signal Analysis in Medical Imaging, 2nd edn., pp. 197–243. Academic Press (2014)

    Google Scholar 

  25. Quan, R., Dong, X., Wu, Y., Zhu, L., Yang, Y.: Auto-ReID: searching for a part-aware convnet for person re-identification. In: IEEE International Conference on Computer Vision, pp. 3749–3758 (2019)

    Google Scholar 

  26. Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton University Press, Princeton (1976)

    Google Scholar 

  27. Tian, M., et al.: Eliminating background-bias for robust person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5794–5803 (2018)

    Google Scholar 

  28. Varior, R.R., Haloi, M., Wang, G.: Gated Siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_48

    Chapter  Google Scholar 

  29. Wang, P., Qing, C., Xu, X., Cai, B., Jin, J., Ren, J.: Local-global extraction unit for person re-identification. In: International Conference on Brain Inspired Cognitive Systems, pp. 402–411 (2018)

    Google Scholar 

  30. Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1249–1258 (2016)

    Google Scholar 

  31. Yager, R., Gupta, M., Kandel, A., Bandler, W., Kiszka, J.: Forms of multi-criteria decision functions and preference information types. In: Approximate Reasoning in Expert Systems, pp. 167–177 (1985)

    Google Scholar 

  32. Yao, H., Zhang, S., Hong, R., Zhang, Y., Xu, C., Tian, Q.: Deep representation learning with part loss for person re-identification. IEEE Trans. Image Process. 28(6), 2860–2871 (2019)

    Article  MathSciNet  Google Scholar 

  33. Yu, R., Zhou, Z., Bai, S., Bai, X.: Divide and fuse: a re-ranking approach for person re-identification. In: The British Machine Vision Conference (BMVC), pp. 135.1–135.13. BMVA Press (2017)

    Google Scholar 

  34. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3–28 (1978)

    Article  MathSciNet  Google Scholar 

  35. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)

    Google Scholar 

  36. Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person reidentification. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 14(1), 1–20 (2017)

    Google Scholar 

  37. Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1318–1327. IEEE (2017)

    Google Scholar 

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Acknowledgement

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Author information

Authors and Affiliations

  1. Digital Research Center of Sfax, 3021, Sfax, Tunisia

    Ilef Ben Slima, Sourour Ammar, Mahmoud Ghorbel & Yousri Kessentini

  2. MIRACL Laboratory, Sfax University, Sfax, Tunisia

    Sourour Ammar & Yousri Kessentini

  3. LIPAH Laboratory, University of Tunis El Manar, Tunis, Tunisia

    Ilef Ben Slima

  4. ReDCAD Laboratory, Sfax University, Sfax, Tunisia

    Mahmoud Ghorbel

Authors
  1. Ilef Ben Slima
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  2. Sourour Ammar
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  3. Mahmoud Ghorbel
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  4. Yousri Kessentini
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Corresponding author

Correspondence to Ilef Ben Slima .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Digital Research Center of Sfax, Sfax, Tunisia

    Yousri Kessentini

  3. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  4. Digital Research Centre of Sfax, Sfax, Tunisia

    Mohamed Jmaiel

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Ben Slima, I., Ammar, S., Ghorbel, M., Kessentini, Y. (2021). Possibilistic Classifier Combination for Person Re-identification. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-71804-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71803-9

  • Online ISBN: 978-3-030-71804-6

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