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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Author: search.filters.author.Chou, Chien-LiSubject: search.filters.subject.video copy detection

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Now showing 1 - 3 of 3
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    NEAR-DUPLICATE VIDEO RETRIEVAL BY USING PATTERN-BASED PREFIX TREE AND TEMPORAL RELATION FOREST
    (2014-01-01)
    Chou, Chien-Li
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    Chen, Hua-Tsung
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    Hsu, Chun-Chieh
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    Ho, Chien-Peng
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    Lee, Suh-Yin
    With the explosive growth of the social multimedia sharing, copyright protection and search result refinement are always the critical issues for the service operators. To resolve the problems, content-based near-duplicate video retrieval is developed in recent years. In this paper, we construct a condensed Pattern-based Prefix tree (PP-tree) to index the patterns of reference videos for fast retrieval. To calculate how likely a query video and a reference video are near-duplicates, a novel algorithm for discovering the temporal relations among patterns is proposed. Comprehensive experiments on public datasets are conducted to verify the effectiveness and efficiency of the proposed method. Experimental results show that the proposed near-duplicate video retrieval approach outperforms the state-of-the-art approaches in terms of precision, recall, and execution time.
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    NEAR-DUPLICATE VIDEO RETRIEVAL AND LOCALIZATION USING PATTERN SET BASED DYNAMIC PROGRAMMING
    (2013-01-01)
    Chou, Chien-Li
    ;
    Chen, Hua-Tsung
    ;
    Chen, Yi-Cheng
    ;
    Ho, Chien-Peng
    ;
    Lee, Suh-Yin
    With the exponential growth of the web multimedia contents, illegal video copies are widespread and easy to be obtained from search engines and video sharing websites. For video copyright protection, near-duplicate video retrieval becomes more and more important. In this paper, we proposed a Pattern Set based Dynamic Programming (PSDP) algorithm to retrieve near-duplicate videos efficiently and effectively. In addition, the precise positions of the near-duplicate segments in videos can also be located. To better deal with the slow/fast motion and the dropped frame problem, the Time-shift Pattern set Similarity (TPS) is then applied. Two datasets are used to evaluate the effectiveness and efficiency of the proposed method, and the experimental results show that the proposed method outperforms the compared approaches in terms of precision and execution time in both two datasets.
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    Pattern-Based Near-Duplicate Video Retrieval and Localization on Web-Scale Videos
    (2015-03-01)
    Chou, Chien-Li
    ;
    Chen, Hua-Tsung
    ;
    Lee, Suh-Yin
    ;
    資訊工程學系
    ;
    Department of Computer Science
    With the exponential growth of web multimedia contents, the Internet is rife with near-duplicate videos, the video copies applied with visual/temporal transformations and/or post productions. Two critical issues, copyright infringement and search result redundancy, arise accordingly. To resolve these problems, this paper proposes a spatiotemporal pattern-based approach under the hierarchical filter-and-refine framework for efficient and effective near-duplicate video retrieval and localization. Firstly, non-near-duplicate videos are fast filtered out through a computationally efficient data structure, termed pattern-based index tree (PI-tree). Then, an m-pattern-based dynamic programming (mPDP) algorithm is designed to localize near-duplicate segments and to re-rank the videos retrieved. The influence of time shift misalignment can be alleviated by time-shift m-pattern similarity (TPS) measurement. Comprehensive experiments on the five datasets are conducted to verify the effectiveness, efficiency, robustness, and scalability of the proposed approach. Convincing results demonstrate that our proposed approach outperforms the state-of-the-art approaches in terms of mean average precision (MAP) and normalized detection cost rate (NDCR) on the testing datasets. Furthermore, the proposed approach can achieve high quality of near-duplicate video localization in terms of quality frames (QF) and mean F1.