Relevance feedback for real-world human action retrieval

Simon Jones, Ling Shao, Jianguo Zhang, Yan Liu

    Research output: Contribution to journalArticle

    32 Citations (Scopus)

    Abstract

    Content-based video retrieval is an increasingly popular research field, in large part due to the quickly growing catalogue of multimedia data to be found online. Even though a large portion of this data concerns humans, however, retrieval of human actions has received relatively little attention. Presented in this paper is a video retrieval system that can be used to perform a content-based query on a large database of videos very efficiently. Furthermore, it is shown that by using ABRS-SVM, a technique for incorporating Relevance feedback (RF) on the search results, it is possible to quickly achieve useful results even when dealing with very complex human action queries, such as in Hollywood movies. (C) 2011 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)446-452
    Number of pages7
    JournalPattern Recognition Letters
    Volume33
    Issue number4
    DOIs
    Publication statusPublished - Mar 2012

    Keywords

    • Content-based video retrieval
    • Relevance feedback
    • Human action recognition
    • TIME INTEREST POINTS

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