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Linear GP with Redundancy-removed Recombination for Synthesis of Image Feature Extraction Programs
https://ipsj.ixsq.nii.ac.jp/records/58825
https://ipsj.ixsq.nii.ac.jp/records/58825f363b59d-f9a3-48de-96b0-2aa4eadbced3
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-BIO08015009.pdf (1.6 MB)
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Copyright (c) 2008 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | SIG Technical Reports(1) | |||||||
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| 公開日 | 2008-12-10 | |||||||
| タイトル | ||||||||
| タイトル | Linear GP with Redundancy-removed Recombination for Synthesis of Image Feature Extraction Programs | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | Linear GP with Redundancy-removed Recombination for Synthesis of Image Feature Extraction Programs | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||
| 資源タイプ | technical report | |||||||
| 著者所属 | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属 | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属 | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属 | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属 | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Department of Media Science, Graduate School of Information Science, Nagoya University | ||||||||
| 著者名 |
Ukrit, Watchareeruetai
× Ukrit, Watchareeruetai
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| 著者名(英) |
Ukrit, Watchareeruetai
× Ukrit, Watchareeruetai
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| 論文抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | We describe an evolutionary synthesis of feature extraction program for object recognition. The evolutionary synthesis method is based on linear genetic programming with the use of redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem without any domain-specific knowledge. An experiment was done on a lawn weed detection problem. The result shows that performances of the synthesized feature extraction program are comparable with those of the conventional lawn weed detection methods. | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | We describe an evolutionary synthesis of feature extraction program for object recognition. The evolutionary synthesis method is based on linear genetic programming with the use of redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem without any domain-specific knowledge. An experiment was done on a lawn weed detection problem. The result shows that performances of the synthesized feature extraction program are comparable with those of the conventional lawn weed detection methods. | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AA12055912 | |||||||
| 書誌情報 |
情報処理学会研究報告バイオ情報学（BIO） 巻 2008, 号 126(2008-BIO-015), p. 33-36, 発行日 2008-12-10 |
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| Notice | ||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
