| Project/Area Number |
08455174
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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 |
情報通信工学
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
SAKAI Yoshinori Tokyo Institute of Technology Faculty of Engineering, Professor, 工学部, 教授 (70196054)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAOKA Katsunori Tokyo Institute of Technology Faculty of Engineering, Assistant Prof., 工学部, 助手 (90262279)
YOSHIDA Toshiyuki Tokyo Institute of Technology Faculty of Engineering, Associate Prof., 工学部, 助教授 (50240297)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥7,600,000 (Direct Cost: ¥7,600,000)
Fiscal Year 1997: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1996: ¥6,600,000 (Direct Cost: ¥6,600,000)
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| Keywords | image retrieval / content retrieval / agent / information retrieval / super-distributed network / 情報探索 / エントロピー / データベース / 色情報 / 通信ネットワーク / 特徴抽出 |
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| Research Abstract |
This research project includes the searching method for image information (target image) in super distributed environment and image retrieval based on image feature.
First, we have studied the searching method for image information in the case that probability of target image location in each database is a priori known. The communication cost and time which is required to search each database is assumed to be given. We have proposed the searching method which makes the estimated communication cost and time minimum. According to the mathematical analysis, it is shown that the proposed method gives better performance compared with random search even if a priori probabilities of target location contains some error. Further, we have developed the prototype system of agent which has capabilities to correct the information concerning the images stored in the network.
Second, we have studied two types of image retrieval methods based on feature information which can be extracted from images aut
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omatically. The one is called "interactive method" and the other is called "keyword method". In the interactive method, the system shows the user plural number of image examples, and the user select the image which is most similar with the target one. The system shows the other images based on the user's selection. After the repetition of display and selection, the system knows the feature information which is included in the target image and retrieves it. We have made the prototype of the interactive method and now the prototype is available through Internet. In the keyword method, we have studied the automatic conversion from keyword to feature information. The rules and parameters which is used in feature matching corresponding to each keyword is obtained through learning procedure and is stored in the dictionary. Genetic algorithm is used to obtain the parameter. The basic study shows that it is possible to convert keyword to feature information if a little conversion error is allowed. Less
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