Color Feature Extraction of the Regions Using the GA for the Scenery Image Retrieval
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- Mitsukura Yasue
- Mitsukura Lab., Tokyo University of Agriculture and Technology
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- Sakamoto Koji
- Mitsukura Lab., Tokyo University of Agriculture and Technology
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- Fukai Hironobu
- Mitsukura Lab., Tokyo University of Agriculture and Technology
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- Yoshimori Seiki
- Nippon Bunri University
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- Ito Seiji
- Hiroshima Institute of Technology
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- Fukumi Minoru
- The University of Tokushima
Bibliographic Information
- Other Title
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- 風景画像検索のための遺伝的アルゴリズムを用いた画像領域の色特徴量取得
- フウケイ ガゾウ ケンサク ノ タメ ノ イデンテキ アルゴリズム オ モチイタ ガゾウ リョウイキ ノ イロ トクチョウリョウ シュトク
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Description
Recently, the keyword image retrieval is widely studied. By using these technologies, we can obtain the images with the corresponding keywords easily. In case of conventional image search systems, we search according to the file names basically. However, filenames which is named are frequently incorrect. To resolve this problem, we propose the automatic keyword addition method for scene images. In this paper, there are two important points. One of them is the image segmentation method using the maximum distance algorithm (MDA). The other is the automatic keyword addition using the color feature of regions. The other is the color feature extraction of regions. In the image segmentation method, we propose the automatic decision method of parameters in the MDA. For this purpose, we investigate the relation between the optimal parameters and features of regions. In the color feature extraction of regions, we propose the genetic algorithm(GA). Moreover, in order to show the effectiveness of the proposed method, we show the simulation examples. According to the results of the simulations, we achieve the keyword addition for scene images.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 129 (4), 710-719, 2009
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390282679582599296
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- NII Article ID
- 10024774841
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 10251228
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL Search
- Crossref
- CiNii Articles
- OpenAIRE
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- Abstract License Flag
- Disallowed