Deep supervised hashing hash has been widely utilized in large-scale image retrieval due to its lightweight storage and fast search speed. The distribution of features from the existing hashing methods has been inevitably distorted from the original feature distribution, which resulted in the performance decline on image retrieval. With the constraints of the Euclidian metric, the distortion can be mitigated to certain extent. However, the Euclidian distance is sensitive to the norm of the features. On the point, we propose a novel deep hashing method, called cosine metric supervised deep hashing (CMDH), to perform hash learning by incorporating cosine metric and category loss. CMDH uses the cosine metric to effectively mitigate the impact caused by the diverse sample vector norms on retrieval performance. In addition, joint deep hash learning constructs binary coding over cosine loss and category loss, which further alleviates distortion in binary feature learning for efficient retrieval.


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    Titel :

    Cosine Metric Supervised Deep Hashing


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Hu, Wenjin (Autor:in) / Chen, Yukun (Autor:in) / Jian, Meng (Autor:in) / Wu, Lifang (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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