Fast and accurate content-based semantic search in 100M Internet Videos

Jiang, L; Yu, SI; Meng, D; Yang, Y; Mitamura, T; Hauptmann, AG

Fast and accurate content-based semantic search in 100M Internet Videos

Jiang, L Yu, SI Meng, D Yang, Y

Mitamura, T Hauptmann, AG

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Publication Type:: Conference Proceeding
Citation:: MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 49 - 58
Issue Date:: 2015-10-13

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Field	Value	Language
dc.contributor.author	Jiang, L	en_US
dc.contributor.author	Yu, SI	en_US
dc.contributor.author	Meng, D	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Mitamura, T	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2015-10-13	en_US
dc.identifier.citation	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 49 - 58	en_US
dc.identifier.isbn	9781450334594	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121772
dc.description.abstract	© 2015 ACM. Large-scale content-based semantic search in video is an interesting and fundamental problem in multimedia analysis and retrieval. Existing methods index a video by the raw concept detection score that is dense and inconsistent, and thus cannot scale to "big data" that are readily available on the Internet. This paper proposes a scalable solution. The key is a novel step called concept adjustment that represents a video by a few salient and consistent concepts that can be efficiently indexed by the modified inverted index. The proposed adjustment model relies on a concise optimization framework with interpretations. The proposed index leverages the text-based inverted index for video retrieval. Experimental results validate the efficacy and the efficiency of the proposed method. The results show that our method can scale up the semantic search while maintaining state-of-Theart search performance. Specifically, the proposed method (with reranking) achieves the best result on the challenging TRECVID Multimedia Event Detection (MED) zeroexample task. It only takes 0.2 second on a single CPU core to search a collection of 100 million Internet videos.	en_US
dc.relation.ispartof	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/2733373.2806237	en_US
dc.title	Fast and accurate content-based semantic search in 100M Internet Videos	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 ACM. Large-scale content-based semantic search in video is an interesting and fundamental problem in multimedia analysis and retrieval. Existing methods index a video by the raw concept detection score that is dense and inconsistent, and thus cannot scale to "big data" that are readily available on the Internet. This paper proposes a scalable solution. The key is a novel step called concept adjustment that represents a video by a few salient and consistent concepts that can be efficiently indexed by the modified inverted index. The proposed adjustment model relies on a concise optimization framework with interpretations. The proposed index leverages the text-based inverted index for video retrieval. Experimental results validate the efficacy and the efficiency of the proposed method. The results show that our method can scale up the semantic search while maintaining state-of-Theart search performance. Specifically, the proposed method (with reranking) achieves the best result on the challenging TRECVID Multimedia Event Detection (MED) zeroexample task. It only takes 0.2 second on a single CPU core to search a collection of 100 million Internet videos.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121772