Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification

Zhang, Zhong and Huang, Meiyan and Liu, Shuang and Xiao, Baihua and Durrani, Tariq S. (2020) Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification. IEEE Transactions on Fuzzy Systems, 28 (7). pp. 1356-1368. ISSN 1063-6706 (https://doi.org/10.1109/TFUZZ.2019.2914626)

[thumbnail of Zhang-etal-IEEE-TFS-2020-Fuzzy-multilayer-clustering-and-fuzzy-label-regularization-for-unsupervised]
Preview
 Text. Filename: Zhang_etal_IEEE_TFS_2020_Fuzzy_multilayer_clustering_and_fuzzy_label_regularization_for_unsupervised.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

Unsupervised person reidentification has received more attention due to its wide real-world applications. In this paper, we propose a novel method named fuzzy multilayer clustering (FMC) for unsupervised person reidentification. The proposed FMC learns a new feature space using a multilayer perceptron for clustering in order to overcome the influence of complex pedestrian images. Meanwhile, the proposed FMC generates fuzzy labels for unlabeled pedestrian images, which simultaneously considers the membership degree and the similarity between the sample and each cluster. We further propose the fuzzy label regularization (FLR) to train the convolutional neural network (CNN) using pedestrian images with fuzzy labels in a supervised manner. The proposed FLR could regularize the CNN training process and reduce the risk of overfitting. The effectiveness of our method is validated on three large-scale person reidentification databases, i.e., Market-1501, DukeMTMC-reID, and CUHK03.

  • Item type:Article
    ID code:73988
    Dates:
    Date
    Event
    31 July 2020
    Published
    2 May 2019
    Published Online
    29 April 2019
    Accepted
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:25 Sep 2020 11:23
    Last modified:17 Apr 2024 00:41
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/73988
CORE (COnnecting REpositories)