Presentation | 2006-03-16 Visual Impression Modeling Using Multiple Instance Learning Masahiro TADA, Zhongfei (MARK) ZHANG, Toshikazu KATO, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Visual Impression / Visual KANSEI / MIL / SVM / Automatic Image Classification |
Paper # | PRMU2005-235 |
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Committee | PRMU |
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Conference Date | 2006/3/9(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Media Understanding (PRMU) |
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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 |
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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 |
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