The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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",
}

The Robotics Institute

Menu