Semantic Analysis for Video Contents Extraction - Spotting by Association in News Video

Yuichi Nakamura and Takeo Kanade
Fifth ACM International Multimedia Conference, November, 1997.


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Abstract
Spotting by Association method for video analysis is a novel metliod to detect video segments with typical semantics. Video data contains various kinds of information through continuous images, natural language, and sound. For videos to be stored and retrieved in a Digital Library, it is essential to segment the video data into meaningful pieces. To detect meaningful segments, we need to identify the segment in each modality (video, language, and sound) that corresponds to the same story. For this purpose, we propose a new method for making correspondences between image clues detected by image analysis and Ianguage clues detected by natural language analysis. As a result, relevant video segments with sufficient information from every modality are obtained. We applied our method to closed-captioned CNN Headline News. Video segments with important events, such as a public speech, meeting, or visit. are detected fairly well.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference
Yuichi Nakamura and Takeo Kanade, "Semantic Analysis for Video Contents Extraction - Spotting by Association in News Video," Fifth ACM International Multimedia Conference, November, 1997.

BibTeX Reference
@inproceedings{Kanade_1997_944,
   author = "Yuichi Nakamura and Takeo Kanade",
   title = "Semantic Analysis for Video Contents Extraction - Spotting by Association in News Video",
   booktitle = "Fifth ACM International Multimedia Conference",
   month = "November",
   year = "1997",
}