Details
| Original language | English |
|---|---|
| Title of host publication | 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 |
| Subtitle of host publication | Proceedings |
| Pages | 1749-1752 |
| Number of pages | 4 |
| Publication status | Published - 26 Dec 2006 |
| Externally published | Yes |
| Event | 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Toronto, ON, Canada Duration: 9 Jul 2006 → 12 Jul 2006 |
Publication series
| Name | 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings |
|---|---|
| Volume | 2006 |
Abstract
The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approaches often make use of thresholding techniques or supervised learning which requires labeling of possibly large training sets. Furthermore, the application of the same training model or parameters might lead to a suboptimal indexing accuracy for a given video. In this paper, we propose to use a novel self-supervised learning framework for robust video indexing to address this issue. Based on an initial classification result for a given video, the best features are selected by Adaboost and are then used to train SVM (support vector machine) classifiers, all on the given video. Finally, a specialized ensemble of classifiers is employed for the given video for decision making. Experimental results show that a state-of-the-art video cut detection approach can be significantly improved by the self-supervised learning approach.
ASJC Scopus subject areas
- Engineering(all)
- Media Technology
- Engineering(all)
- Electrical and Electronic Engineering
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2006 IEEE International Conference on Multimedia and Expo, ICME 2006 : Proceedings. 2006. p. 1749-1752 4036958 (2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings; Vol. 2006).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Self-supervised learning for robust video indexing
AU - Ewerth, Ralph
AU - Freisleben, Bernd
PY - 2006/12/26
Y1 - 2006/12/26
N2 - The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approaches often make use of thresholding techniques or supervised learning which requires labeling of possibly large training sets. Furthermore, the application of the same training model or parameters might lead to a suboptimal indexing accuracy for a given video. In this paper, we propose to use a novel self-supervised learning framework for robust video indexing to address this issue. Based on an initial classification result for a given video, the best features are selected by Adaboost and are then used to train SVM (support vector machine) classifiers, all on the given video. Finally, a specialized ensemble of classifiers is employed for the given video for decision making. Experimental results show that a state-of-the-art video cut detection approach can be significantly improved by the self-supervised learning approach.
AB - The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approaches often make use of thresholding techniques or supervised learning which requires labeling of possibly large training sets. Furthermore, the application of the same training model or parameters might lead to a suboptimal indexing accuracy for a given video. In this paper, we propose to use a novel self-supervised learning framework for robust video indexing to address this issue. Based on an initial classification result for a given video, the best features are selected by Adaboost and are then used to train SVM (support vector machine) classifiers, all on the given video. Finally, a specialized ensemble of classifiers is employed for the given video for decision making. Experimental results show that a state-of-the-art video cut detection approach can be significantly improved by the self-supervised learning approach.
UR - http://www.scopus.com/inward/record.url?scp=34247597368&partnerID=8YFLogxK
U2 - 10.1109/ICME.2006.262889
DO - 10.1109/ICME.2006.262889
M3 - Conference contribution
AN - SCOPUS:34247597368
SN - 1424403677
SN - 9781424403677
T3 - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
SP - 1749
EP - 1752
BT - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006
T2 - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006
Y2 - 9 July 2006 through 12 July 2006
ER -