Presentation 2006-03-16
Visual Impression Modeling Using Multiple Instance Learning
Masahiro TADA, Zhongfei (MARK) ZHANG, Toshikazu KATO,
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Abstract(in English) Most of digital contents distributers use key words which correspond to objects in images to index various kinds of photo images. But these key words not always match with visual impression of images. In this paper, we propose a method to evaluate visual impression of images by using image key words. By statistically analysing typical photo examples of each image key word given by professional photographers, we have modeled their image evaluation process based on visual impressions (KANSEI Model). By using the KANSEI model, we have developed automatic image classification system for various kinds of photo images based on visual impressions.
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Keyword(in English) Visual Impression / Visual KANSEI / MIL / SVM / Automatic Image Classification
Paper # PRMU2005-235
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Conference Information
Committee PRMU
Conference Date 2006/3/9(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) Visual Impression Modeling Using Multiple Instance Learning
Sub Title (in English)
Keyword(1) Visual Impression
Keyword(2) Visual KANSEI
Keyword(3) MIL
Keyword(4) SVM
Keyword(5) Automatic Image Classification
1st Author's Name Masahiro TADA
1st Author's Affiliation ATR Media Information Science Laboratories()
2nd Author's Name Zhongfei (MARK) ZHANG
2nd Author's Affiliation Dept. of Computer Science, Watson School of Engineering and Applied Sciences, Binghamton University
3rd Author's Name Toshikazu KATO
3rd Author's Affiliation Fac. of Science and Engineering, Chuo University
Date 2006-03-16
Paper # PRMU2005-235
Volume (vol) vol.105
Number (no) 673
Page pp.pp.-
#Pages 6
Date of Issue