Presentation 2008-12-18
Video Image Analysis Exploiting Frequent Graph Mining
Tomokazu TSUJI, Hisashi KOGA, Takanori YOKOYAMA, Toshinori WATANABE,
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Abstract(in English) Frequent graph mining is a technique to extract graph patterns which appear frequently in large set of graphs as useful patterns. This paper proposes to apply frequent graph mining to video image analysis. This paper deals with a video in which moving object passes in front of a surveillance camera and devices a graph-based background subtraction method exploiting frequent graph mining. In our method, every video frame is segmented into regions first by some image segmentation. Next, the video frame is converted into a region adjacency graph such that regions correspond to graph nodes and the region adjacency is represented by graph edges. Then, the background removal is performed by discovering the background as a frequent graph.
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Keyword(in English) Graph Mining / Video Image Analysis / Data Compression / Background Removal
Paper # PRMU2008-150
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Conference Information
Committee PRMU
Conference Date 2008/12/11(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) Video Image Analysis Exploiting Frequent Graph Mining
Sub Title (in English)
Keyword(1) Graph Mining
Keyword(2) Video Image Analysis
Keyword(3) Data Compression
Keyword(4) Background Removal
1st Author's Name Tomokazu TSUJI
1st Author's Affiliation Graduate School of Information Systems, University of Electro-Communications()
2nd Author's Name Hisashi KOGA
2nd Author's Affiliation Graduate School of Information Systems, University of Electro-Communications
3rd Author's Name Takanori YOKOYAMA
3rd Author's Affiliation Graduate School of Information Systems, University of Electro-Communications
4th Author's Name Toshinori WATANABE
4th Author's Affiliation Graduate School of Information Systems, University of Electro-Communications
Date 2008-12-18
Paper # PRMU2008-150
Volume (vol) vol.108
Number (no) 363
Page pp.pp.-
#Pages 6
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