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クラスタ型コンピュータを用いた Genetic Image Network for Image Classification の並列処理による高速化
http://hdl.handle.net/10458/4743
http://hdl.handle.net/10458/474379d735ba-1b3f-4ea1-bcb7-dd5eab9a315e
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
engineering245-249.pdf (927.7 kB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2014-01-06 | |||||
| タイトル | ||||||
| タイトル | クラスタ型コンピュータを用いた Genetic Image Network for Image Classification の並列処理による高速化 | |||||
| 言語 | ja | |||||
| タイトル | ||||||
| タイトル | Accelerating of Genetic Image Network for Image Classification by Parallel Computation using Computer Cluster | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Genetic programming, Image classification, Parallel processing, Cluster computer | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | departmental bulletin paper | |||||
| その他（別言語等）のタイトル | ||||||
| その他のタイトル | クラスタガタ コンンピュータ オ モチイタ Genetic Image Network for Image Classification ノ ヘイレツ ショリ ニヨル コウソクカ | |||||
| 言語 | ja-Kana | |||||
| 著者 |
山森, 一人
× 山森, 一人× 吉田, 卓矢× 相川, 勝× Yoshida, Takuya |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Recently, the number of digital images we have is increasing as popularizing cellular phone with digital camera. To manage these digital images, some researches investigate new technologies for automatic image classification. Since image classification technologies depends on target images, it is difficult to decide what kinds of techniques and classification threshold should be used. To solve this problem, a technique, called as Genetic Image Network for Image Classification (GIN-IC), has been proposed that builds an image classification algorithm automatically. However, GIN-IC requires a huge computation time. In this paper, we propose a high-speed GIN-IC with master-worker parallel computation model. A master broadcasts an individual to all workers, them workers compute the fitness in parallel by using a part of training images. These fitnesses are collected to the master, and the master calculates final fitness by summing up all the collected fitness. We implement our method in a cluster computer, and evaluate computation time until building suitable algorithm that classifies digital images into two classes. Our method achieved four times faster than that of serial model with a master and six workers. | |||||
| 言語 | en | |||||
| 書誌情報 |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 42, p. 245-249, 発行日 2013-08-30 |
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| 出版者 | ||||||
| 出版者 | 宮崎大学工学部 | |||||
| 言語 | ja | |||||
| 出版者 | ||||||
| 出版者 | Faculty of Engineering, University of Miyazaki | |||||
| 言語 | en | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 05404924 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA00732558 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
