End-to-End Correspondence and Relationship Learning of Mid-Level Deep Features for Person Re-Identification

Shan Lin, Chang-Tsun Li

    Research output: Book chapter/Published conference paperConference paperpeer-review

    2 Citations (Scopus)

    Abstract

    In this paper, a unified deep convolutional architecture is proposed to address the problems in the person re-identification task. The proposed method adaptively learns the discriminative deep mid-level features of a person and constructs the correspondence features between an image pair in a data-driven manner. The previous Siamese structure deep learning approaches focus only on pair-wise matching between features. In our method, we consider the latent relationship between mid-level features and propose a network structure to automatically construct the correspondence features from all input features without a pre-defined matching function. The experimental results on three benchmarks VIPeR, CUHK01 and CUHK03 show that our unified approach improves over the previous state-of-the-art methods.
    Original languageEnglish
    Title of host publicationProceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-6
    Number of pages6
    DOIs
    Publication statusPublished - 21 Dec 2017
    Event2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Novotel Sydney Manly Pacific, Sydney, Australia
    Duration: 29 Nov 201701 Dec 2017
    http://dicta2017.dictaconference.org/index.html (Conference website)
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 (Conference proceedings)

    Conference

    Conference2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Country/TerritoryAustralia
    CitySydney
    Period29/11/1701/12/17
    OtherThe International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS).
    Internet address

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