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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))

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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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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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