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
Cite this
Sequential pattern based temporal contour representations for content-based multimedia timeline analysis. / Ren, Gang; Johnson, Joseph; Lee, Hyunhwan; Ogihara, Mitsunori.
Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 657-664 7838220.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Sequential pattern based temporal contour representations for content-based multimedia timeline analysis
AU - Ren, Gang
AU - Johnson, Joseph
AU - Lee, Hyunhwan
AU - Ogihara, Mitsunori
PY - 2017/1/31
Y1 - 2017/1/31
N2 - 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.
AB - 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.
KW - Contour representations
KW - Multimedia signal processing
KW - Multimedia structure analysis
KW - Sequential pattern
UR - http://www.scopus.com/inward/record.url?scp=85015443639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015443639&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2016.159
DO - 10.1109/ICMLA.2016.159
M3 - Conference contribution
AN - SCOPUS:85015443639
SP - 657
EP - 664
BT - Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -