| Project/Area Number |
14580424
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu University (2004)
九州芸術工科大学 (2002-2003) |
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Principal Investigator |
URAHAMA Kiichi Kyushu University, Faculty of Design, Professor, 大学院・芸術工学研究院, 教授 (10150492)
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| Co-Investigator(Kenkyū-buntansha) |
INOUE Kohei Kyushu University, Faculty of Design, Research Associate, 大学院・芸術工学研究院, 助手 (70325570)
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| Project Period (FY) |
2002 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥2,800,000 (Direct Cost: ¥2,800,000)
Fiscal Year 2004: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2003: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2002: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | video data / similarity search / collaborative filtering / filtering search / latent semantic analysis / clustering / graph spectral method / video analysis / 映像検索 / 類似画像検索 / 強調フィルタリング / 時間コレログラム |
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| Research Abstract |
The aims of this research is the similarity search of visual media. We have developed some basic techniques for personalizing retrieval systems such as interactive retrieval methods based on relevance feedbacks, speeding-up techniques by filtering retrieval methods and user preference handling methods based on collaborative filtering techniques.
At first, we have developed some image retrieval methods such as the similarity search by using edge orientation histograms, retrieval with a graph histogram based on extraction of image feature points and an image retrieval technique using bilinear form similarity between the feature vectors.
We have next applied these image retrieval methods to the similarity search of video frames. We developed firstly a video retrieval method based on the distance between mixture distributions after the development of the representation method for videos with mixture distributions of frames. We next proposed a feature vector of videos called the temporal corr
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elogram which represents the correlation between consecutive video frames and by using this temporal correlogram we have developed a similarity search technique for videos. We have also developed a similarity search method of videos based on the latent semantic analysis with the representation of the video database by a contingency table of shot configuration of videos. We have developed a robust clustering method for the improvement in the performance of this video retrieval method based on the latent semantic analysis.
At the aim of acceleration of the search of images or videos based on these methods, we have developed some filtering search techniques such as the filtering based on the lower bound of the bilinear form distance between feature vectors, the filtering based on the upper bound of the temporal correlogram, the filtering based on the lower bound of the graph histograms and the filtering search technique based on the upper and the lower bounds of the histogram intersection.
We have examined the collaborative filtering method for recommendation of videos for users according to their predicted preference of videos. We have developed an acceleration method for the collaborative filtering by clustering users, a fast collaborative filtering method of preference estimation based on the correlation between items, and a method for selecting an optimal set of items for presenting them to a new user. Less
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