2004 Fiscal Year Final Research Report Summary
A Research on Extraction and Application of Video Keyword based-on Large-Scale Video Archive
| Project/Area Number |
15500082
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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 |
Media informatics/Database
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| Research Institution | National Institute of Informatics |
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Principal Investigator |
SATOH Shinichi National Institute of Informatics, Multimedia Information Research Division, Professor, 情報メディア研究系, 教授 (90249938)
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| Project Period (FY) |
2003 – 2004
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| Keywords | Image matching / Histogram / Dimension reduction / Video indexing / Video browsing / Identical video segment detection |
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| Research Abstract |
We proposed a technique to extract "video keywords" which are characteristic regions in videos, and we studied its applications. Video keywords can be identified each other in videos, and thus they enable video indexing based on characteristic objects, buildings, people, particular regions in background, and iconic regions, which ordinary approaches for typical image retrieval using indexing by global color distribution cannot handle.
Video keyword extraction requires image segmentation to locate object region in general, however, it is said that the perfect segmentation is impossible without semantics information of the contents of the image, which cannot be assumed. Therefore video keyword extraction can be hard task. On the other hand, keyword extraction for text analysis is robustly realized based on statistical information of the occurrence of each keyword in large-scale text corpus, such as TFIDF, to select important words, i.e., keyword extraction does not rely on semantics information. Basic idea of the presented research is to apply similar technique to video domain, i.e., extract frequently used video segments as video keywords assuming that they correspond to important visual information.
It obviously requires face image matching to realize video keyword extraction from large-scale video archives. So we proposed Generalized Histogram as an optimized feature representation based on luminosity distribution histogram, and its effectiveness was demonstrated. Moreover, the image browser using identical video segment detection to extract video keywords was developed to show its practical effectiveness of video keywords. In addition, the quasi high-dimensional clustering method was developed to further speed up the image matching.
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