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    Shot-based video retrieval with optical flow tensor and HMMs

    Gao, X. and Li, Xuelong and Feng, J. and Tao, D. (2009) Shot-based video retrieval with optical flow tensor and HMMs. Pattern Recognition Letters 30 (2), pp. 140-147. ISSN 0167-8655.

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    Video retrieval and indexing research aims to efficiently and effectively manage very large video databases, e.g., CCTV records, which is a key component in video-based object and event analysis. In this paper, for the purpose of video retrieval, we propose a novel method to represent video data by developing an optical flow tensor (OFT) and incorporating hidden Markov models (HMMs). As video is content-sensitive and normally carries rich motion information of objects, optical flow field is first employed to estimate such motion. Then, a shot HMMs tree is built to model video clips in different levels in a database. Experimental results demonstrate that the newly developed method inherits advantages of both optical flow and HMMs in video representation. With the newly developed video representation, in video retrieval and indexing tasks, no need to exhaustively compare a query video shot with all video shot records in the database. Moreover, the novel representation method works well when linear discriminant analysis (LDA) is utilized to reduce the feature dimensionality and further speed up the retrieval procedure.


    Item Type: Article
    Keyword(s) / Subject(s): shot-based video retrieval, optical flow tensor, HMMs, shot HMMs tree
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 11 Jul 2013 15:44
    Last Modified: 11 Oct 2016 15:27


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