Automatic video genre classification using multiple SVM votes

Won Dong Jang, Chulwoo Lee, Jae Young Sim, Chang-Su Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    A video genre classification algorithm based on the voting from multiple SVMs is proposed in this work. While conventional genre classifiers use generic baseline features, we employ more specialized features to describe five video genres: animation, commercial, entertainment, drama, and sports. We also present a robust classification algorithm using multiple SVMs, which consider all possible binary grouping of the five genres. Given a query video, each SVM casts a probabilistic vote for each genre. Then, the optimal genre with the maximum votes is selected. Experimental results show that the proposed algorithm provides more accurate classification performance than conventional algorithms.

    Original languageEnglish
    Title of host publicationProceedings - International Conference on Pattern Recognition
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2655-2660
    Number of pages6
    ISBN (Electronic)9781479952083
    DOIs
    Publication statusPublished - 2014 Dec 4
    Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
    Duration: 2014 Aug 242014 Aug 28

    Publication series

    NameProceedings - International Conference on Pattern Recognition
    ISSN (Print)1051-4651

    Other

    Other22nd International Conference on Pattern Recognition, ICPR 2014
    Country/TerritorySweden
    CityStockholm
    Period14/8/2414/8/28

    Bibliographical note

    Publisher Copyright:
    © 2014 IEEE.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

    Fingerprint

    Dive into the research topics of 'Automatic video genre classification using multiple SVM votes'. Together they form a unique fingerprint.

    Cite this