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    Semantic content analysis for effective video segmentation, summarisation and retrieval.

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    Cover page_Ren.pdf (275.9Kb)
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    Abstract (13.00Kb)
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    Appendix 1.pdf (223.1Kb)
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    ch1-introduction.pdf (210.0Kb)
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    ch2-review.pdf (225.9Kb)
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    ch3_shot detection.pdf (1.475Mb)
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    ch4_frame alignment.pdf (959.0Kb)
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    ch5_video summarization.pdf (1.428Mb)
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    ch6_events.pdf (669.6Kb)
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    ch7-conclusions.pdf (164.6Kb)
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    Reference.pdf (300.2Kb)
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    Table of Content.pdf (309.1Kb)
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    Publication date
    2010-03-10T16:41:55Z
    Author
    Ren, Jinchang
    Supervisor
    Jiang, Jianmin
    Ipson, Stanley S.
    Keyword
    Semantic content analysis
    Shot boundary detection
    Video segmentation
    Subspace phase correlation
    Frame alignment
    Video summarisation
    Hierarchical modelling
    Adaptive clustering
    Content-based retrieval
    Automatic annotation
    TRECVID
    Digital video processing
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    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    Department of Electronic Imaging and Media Communications
    Awarded
    2009
    
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    Abstract
    This thesis focuses on four main research themes namely shot boundary detection, fast frame alignment, activity-driven video summarisation, and highlights based video annotation and retrieval. A number of novel algorithms have been proposed to address these issues, which can be highlighted as follows. Firstly, accurate and robust shot boundary detection is achieved through modelling of cuts into sub-categories and appearance based modelling of several gradual transitions, along with some novel features extracted from compressed video. Secondly, fast and robust frame alignment is achieved via the proposed subspace phase correlation (SPC) and an improved sub-pixel strategy. The SPC is proved to be insensitive to zero-mean-noise, and its gradient-based extension is even robust to non-zero-mean noise and can be used to deal with non-overlapped regions for robust image registration. Thirdly, hierarchical modelling of rush videos using formal language techniques is proposed, which can guide the modelling and removal of several kinds of junk frames as well as adaptive clustering of retakes. With an extracted activity level measurement, shot and sub-shot are detected for content-adaptive video summarisation. Fourthly, highlights based video annotation and retrieval is achieved, in which statistical modelling of skin pixel colours, knowledge-based shot detection, and improved determination of camera motion patterns are employed. Within these proposed techniques, one important principle is to integrate various kinds of feature evidence and to incorporate prior knowledge in modelling the given problems. High-level hierarchical representation is extracted from the original linear structure for effective management and content-based retrieval of video data. As most of the work is implemented in the compressed domain, one additional benefit is the achieved high efficiency, which will be useful for many online applications.
    URI
    http://hdl.handle.net/10454/4251
    Type
    Thesis
    Qualification name
    PhD
    Collections
    Theses

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