Clip-based similarity measure for hierarchical video retrieval

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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Main Authors: PENG, Yuxin, NGO, Chong-wah
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2004
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Online Access:https://ink.library.smu.edu.sg/sis_research/6507
https://ink.library.smu.edu.sg/context/sis_research/article/7510/viewcontent/1026711.1026721.pdf
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spelling sg-smu-ink.sis_research-75102022-01-10T03:58:27Z Clip-based similarity measure for hierarchical video retrieval 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 the visual and granularity factors. Based on MM and OM, a hierarchical video retrieval framework is constructed for the approximate matching of video clips. To allow the matching between a query and a long video, an online clip segmentation algorithm is also proposed to rapidly locate candidate clips for similarity measure. The validity of the retrieval framework is theoretically proved and empirically verified on a video database of 21 hours. 2004-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6507 info:doi/10.1145/1026711.1026721 https://ink.library.smu.edu.sg/context/sis_research/article/7510/viewcontent/1026711.1026721.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-based similarity Hierarchical video retrieval Databases and Information Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Clip-based similarity
Hierarchical video retrieval
Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle Clip-based similarity
Hierarchical video retrieval
Databases and Information Systems
Graphics and Human Computer Interfaces
PENG, Yuxin
NGO, Chong-wah
Clip-based similarity measure for hierarchical video retrieval
description 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 the visual and granularity factors. Based on MM and OM, a hierarchical video retrieval framework is constructed for the approximate matching of video clips. To allow the matching between a query and a long video, an online clip segmentation algorithm is also proposed to rapidly locate candidate clips for similarity measure. The validity of the retrieval framework is theoretically proved and empirically verified on a video database of 21 hours.
format text
author PENG, Yuxin
NGO, Chong-wah
author_facet PENG, Yuxin
NGO, Chong-wah
author_sort PENG, Yuxin
title Clip-based similarity measure for hierarchical video retrieval
title_short Clip-based similarity measure for hierarchical video retrieval
title_full Clip-based similarity measure for hierarchical video retrieval
title_fullStr Clip-based similarity measure for hierarchical video retrieval
title_full_unstemmed Clip-based similarity measure for hierarchical video retrieval
title_sort clip-based similarity measure for hierarchical video retrieval
publisher Institutional Knowledge at Singapore Management University
publishDate 2004
url https://ink.library.smu.edu.sg/sis_research/6507
https://ink.library.smu.edu.sg/context/sis_research/article/7510/viewcontent/1026711.1026721.pdf
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