| Project/Area Number |
13480106
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報システム学(含情報図書館学)
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| Research Institution | Future University -Hakodate (2002)
Kochi University (2001) |
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Principal Investigator |
KONISHI Osamu Dept. of Complex Systems Professor, システム情報科学部, 教授 (00135386)
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| Co-Investigator(Kenkyū-buntansha) |
HONDA Rie Kochi University, Dept. of Information Science Assistant Professor, 理学部, 助手 (80253334)
新美 礼彦 公立はこだて未来大学, システム情報科学部, 助手 (80347179)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥4,800,000 (Direct Cost: ¥4,800,000)
Fiscal Year 2002: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2001: ¥3,100,000 (Direct Cost: ¥3,100,000)
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| Keywords | multimedia data mining / multi-dimensional data / satellite images / dynamic time warping / clustering / Self Organizing Maps / content-based indexing / EM algorithm / 蛋白泳動波形 / 時系列データ / 類似検索システム / 知識発見・データマイニング / 時系列 |
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| Research Abstract |
Multimedia data mining is data mining from multimedia objects, the discovery of patterns from within multimedia objects or a collection of objects as a whole. This research was devoted to some developments in knowledge discovery techniques and application specifically pertaining to large image and video data collections such as satellite images and multi-dimensional medical data. The main paper "Mining of Moving Objects from Time Series Images and its Application to Satellite Weather Imagery'" addresses the issue of mining video sequences or moving objects in a scene with concrete examples. We consider collections of still images, in particular satellite images, but track moving objects within these images considering sequences of images as frames. We adapt a two-stage self-organizing map to cluster images in groups containing similar moving objects, thus identifying prominent objects which are then tracked in the sequence of chronologically ordered images. The framework was applied to weather satellite images to detect and classify seasonal variation tendencies.
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