Learning algorithms and frame signatures for video similarity ranking

Teruki Horie, Akihiro Shikano, Hiromichi Iwase, Yasuo Matsuyama

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

    1 Citation (Scopus)

    Abstract

    Learning algorithms that harmonize standardized video similarity tools and an integrated system are presented. The learning algorithms extract exemplars reflecting time courses of video frames. There were five types of such clustering methods. Among them, this paper chooses a method called time-partition pairwise nearest-neighbor because of its reduced complexity. On the similarity comparison among videos whose lengths vary, the M-distance that can absorb the difference of the exemplar cardinalities is utilized both for global and local matching. Given the order-aware clustering and the M-distance comparison, system designers can build a basic similar-video retrieval system. This paper promotes further enhancement on the exemplar similarity that matches the video signature tools for the multimedia content description interface by ISO/IEC. This development showed the ability of the similarity ranking together with the detection of plagiarism of video scenes. Precision-recall curves showed a high performance in this experiment.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages147-157
    Number of pages11
    Volume9489
    ISBN (Print)9783319265315
    DOIs
    Publication statusPublished - 2015
    Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
    Duration: 2015 Nov 92015 Nov 12

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9489
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other22nd International Conference on Neural Information Processing, ICONIP 2015
    CountryTurkey
    CityIstanbul
    Period15/11/915/11/12

    Keywords

    • Exemplar
    • Frame signature
    • M-distance
    • Numerical label
    • Video similarity ranking

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Fingerprint Dive into the research topics of 'Learning algorithms and frame signatures for video similarity ranking'. Together they form a unique fingerprint.

  • Cite this

    Horie, T., Shikano, A., Iwase, H., & Matsuyama, Y. (2015). Learning algorithms and frame signatures for video similarity ranking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9489, pp. 147-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9489). Springer Verlag. https://doi.org/10.1007/978-3-319-26532-2_17