Presentation 2005-06-16
A Fast Time-series Search Algorithm for Audio and Video Signals Based on Local and Global Feature Clustering
Kunio KASHINO, Akisato KIMURA, Takayuki KUROZUMI,
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Abstract(in English) This paper proposes a quick method of similarity-based video searching to detect and locate a specific video clip given as a query in a stored long video stream. The method employs a two-stage process: local and global feature clustering. The local clustering exploits continuity or local similarities between video features, and the global clustering gathers similar video frames that are not necessarily adjacent to each other. These processes prune irrelevant sections on a video stream. The method guarantees the exactly same search result as the exhaustive search. Experiments performed on a PC show that the proposed method can correctly detect and locate a 7.5-second clip in a 150-hour video recording in 15 ms on average.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) time-series search / fingerprinting / clustering / pruning
Paper # DE2005-3,PRMU2005-24
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Committee PRMU
Conference Date 2005/6/9(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Fast Time-series Search Algorithm for Audio and Video Signals Based on Local and Global Feature Clustering
Sub Title (in English)
Keyword(1) time-series search
Keyword(2) fingerprinting
Keyword(3) clustering
Keyword(4) pruning
1st Author's Name Kunio KASHINO
1st Author's Affiliation NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation()
2nd Author's Name Akisato KIMURA
2nd Author's Affiliation NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation
3rd Author's Name Takayuki KUROZUMI
3rd Author's Affiliation NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation
Date 2005-06-16
Paper # DE2005-3,PRMU2005-24
Volume (vol) vol.105
Number (no) 118
Page pp.pp.-
#Pages 5
Date of Issue