Clip-based similarity measure for query-dependent clip retrieval and video summarization
This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking...
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sg-smu-ink.sis_research-73192021-11-23T05:14:34Z Clip-based similarity measure for query-dependent clip retrieval and video summarization PENG, Yuxin NGO, Chong-wah This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to visual and granularity factors. We apply the similarity measure for two tasks: retrieval and summarization. In video retrieval, a hierarchical retrieval framework is constructed based on MM and OM. The validity of the framework is theoretically proved and empirically verified on a video database of 21 h. A query-dependent clip segmentation algorithm is also proposed to automatically locate the potential boundaries of clips in videos. In video summarization, a graph-based clustering algorithm, incorporated with the proposed similarity measure, is adopted to detect the highlighted events reported by different newscasts. 2006-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6316 info:doi/10.1109/TCSVT.2006.873157 https://ink.library.smu.edu.sg/context/sis_research/article/7319/viewcontent/csvt06.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University clip similarity query-based segmentation hierarchical video retrieval summarization Databases and Information Systems Graphics and Human Computer Interfaces |
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clip similarity query-based segmentation hierarchical video retrieval summarization Databases and Information Systems Graphics and Human Computer Interfaces PENG, Yuxin NGO, Chong-wah Clip-based similarity measure for query-dependent clip retrieval and video summarization |
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This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to visual and granularity factors. We apply the similarity measure for two tasks: retrieval and summarization. In video retrieval, a hierarchical retrieval framework is constructed based on MM and OM. The validity of the framework is theoretically proved and empirically verified on a video database of 21 h. A query-dependent clip segmentation algorithm is also proposed to automatically locate the potential boundaries of clips in videos. In video summarization, a graph-based clustering algorithm, incorporated with the proposed similarity measure, is adopted to detect the highlighted events reported by different newscasts. |
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PENG, Yuxin NGO, Chong-wah |
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PENG, Yuxin NGO, Chong-wah |
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PENG, Yuxin |
title |
Clip-based similarity measure for query-dependent clip retrieval and video summarization |
title_short |
Clip-based similarity measure for query-dependent clip retrieval and video summarization |
title_full |
Clip-based similarity measure for query-dependent clip retrieval and video summarization |
title_fullStr |
Clip-based similarity measure for query-dependent clip retrieval and video summarization |
title_full_unstemmed |
Clip-based similarity measure for query-dependent clip retrieval and video summarization |
title_sort |
clip-based similarity measure for query-dependent clip retrieval and video summarization |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/6316 https://ink.library.smu.edu.sg/context/sis_research/article/7319/viewcontent/csvt06.pdf |
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