Abstract
Temporal contour shapes are closely linked to the narrative structure of multimedia content and provide important reference points in content-based multimedia timeline analysis. In this paper, multimedia timeline is extracted from content as time varying video and audio signal features. A temporal contour representation is implemented based on sequential pattern discovery algorithm for modeling the variation contours of multimedia features. The proposed contour representation extracts repetitive temporal patterns from a hierarchy of time resolutions or from synchronized video/audio feature dimensions. The statistically significant contour components, depicting the dominant timeline shapes, are utilized as a structural or analytical representation of the timeline. The modeling performance of this proposed temporal modeling framework is demonstrated through empirical validation and subjective evaluations.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 657-664 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509061662 |
| DOIs | |
| State | Published - Jan 31 2017 |
| Event | 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States Duration: Dec 18 2016 → Dec 20 2016 |
Other
| Other | 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 |
|---|---|
| Country | United States |
| City | Anaheim |
| Period | 12/18/16 → 12/20/16 |
Keywords
- Contour representations
- Multimedia signal processing
- Multimedia structure analysis
- Sequential pattern
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications