Presentation 2007-06-28
Dynamic Visual Pattern Mining Based on Web Information
Yongqing SUN, Satoshi SHIMADA, MASASHI Morimoto, Yukinobu Taniguchi,
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Abstract(in English) In this paper, we propose a novel solution to automatically build visual concept models exploring web image resources. The only input required is one simple query-word to initialize the modeling system. Regarding the query word, the proposed system first extracts its popular semantic concepts using web text information. Then web images associated with each semantic concept are crawled from the web image resources. At last common visual pattern is mined effectively from the web images with the proposed learning algorithm, in which positive and negative samples of good quality are selected from the noisy web images since the quality of learning algorithms is strongly related to the selection of training samples, The proposed solution enables unlimited, dynamic visual concept modeling, and is robust and reliable for generic images. Experimental results demonstrate the effectiveness of the proposed approach.
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Keyword(in English) Web image mining / Image clustering and retrieval / Unsupervised learning / Dynamic visual pattern
Paper # DE2007-3,PRMU2007-29
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Committee DE
Conference Date 2007/6/21(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Dynamic Visual Pattern Mining Based on Web Information
Sub Title (in English)
Keyword(1) Web image mining
Keyword(2) Image clustering and retrieval
Keyword(3) Unsupervised learning
Keyword(4) Dynamic visual pattern
1st Author's Name Yongqing SUN
1st Author's Affiliation NTT Cyber Solutions Laboratories()
2nd Author's Name Satoshi SHIMADA
2nd Author's Affiliation NTT Cyber Solutions Laboratories
3rd Author's Name MASASHI Morimoto
3rd Author's Affiliation NTT Resonant Inc.
4th Author's Name Yukinobu Taniguchi
4th Author's Affiliation NTT Cyber Solutions Laboratories
Date 2007-06-28
Paper # DE2007-3,PRMU2007-29
Volume (vol) vol.107
Number (no) 114
Page pp.pp.-
#Pages 6
Date of Issue