In this paper, we present a scalable keyframe extraction method using one-class support vector machine. Keyframe extraction seeks to generate "good" images that best represent underlying video content and provide content-based access points. Criteria for "good" images play a major role for keyframe extraction process. Extracting "good images" can be viewed as detecting "novel images" among all the frames within a video. Therefore, keyframe extraction reduces to novelty detection problem. We describe how to efficiently solve the novelty detection problem using one-class support vector machine. We also present an algorithm of extracting keyframes in a scalable way so that one can access a video from coarse to fine resolution. We demonstrate the performance of our algorithm on several different types of videos.
|Number of pages||9|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2003|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)