Semi-supervised video object segmentation using multiple random walkers

Won Dong Jang, Chang-Su Kim

    Research output: Contribution to conferencePaperpeer-review

    10 Citations (Scopus)

    Abstract

    A semi-supervised video object segmentation algorithm using multiple random walkers (MRW) is proposed in this work. We develop an initial probability estimation scheme that minimizes an objective function to roughly separate the foreground from the background. Then, we simulate MRW by employing the foreground and background agents. During the MRW process, we update restart distributions using a hybrid of inference restart rule and interactive restart rule. By performing these processes from the second to the last frames, we obtain a segment track of the target object. Furthermore, we optionally refine the segment track by performing Markov random field optimization. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art conventional algorithms on the SegTrack v2 dataset.

    Original languageEnglish
    Pages57.1-57.13
    DOIs
    Publication statusPublished - 2016
    Event27th British Machine Vision Conference, BMVC 2016 - York, United Kingdom
    Duration: 2016 Sept 192016 Sept 22

    Other

    Other27th British Machine Vision Conference, BMVC 2016
    Country/TerritoryUnited Kingdom
    CityYork
    Period16/9/1916/9/22

    Bibliographical note

    Publisher Copyright:
    © 2016. The copyright of this document resides with its authors.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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