Details
| Original language | English |
|---|---|
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | TREC Video Retrieval Evaluation, TRECVID 2009 - Gaithersburg, MD, United States Duration: 16 Nov 2009 → 17 Nov 2009 |
Conference
| Conference | TREC Video Retrieval Evaluation, TRECVID 2009 |
|---|---|
| Country/Territory | United States |
| City | Gaithersburg, MD |
| Period | 16 Nov 2009 → 17 Nov 2009 |
Abstract
In this paper, we summarize our results for the high-level feature extraction task at TRECVID 2009. Our last year's high-level feature extraction system relied on low-level features as well as on state-of-theart approaches for camera motion estimation, text detection, face detection and audio segmentation. Based on the observation that the use of face detection results improved the performance of several face related concepts, we have incorporated further specialized object detectors. Using specialized object detectors trained on separate public data sets, objectbased features are generated by assembling detection results to object sequences. A shot-based confidence score and additional features, such as position, frame coverage and movement, are computed for each object class. The object detectors are used for two purposes: (a) to provide retrieval results for concepts directly related to the object class (such as using the boat detector for the concept boat), (b) to provide objectbased features as additional input for the SVM-based concept classifiers. Thus, other related concepts can also profit from object-based features. Furthermore, we investigated the use of SURF (Speeded Up Robust Features). The use of object-based features improved the high-level feature extraction results significantly. Our best run achieved a mean inferred average precision of 9.53%.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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2009. Paper presented at TREC Video Retrieval Evaluation, TRECVID 2009, Gaithersburg, MD, United States.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - University of Marburg at TRECVID 2009
T2 - TREC Video Retrieval Evaluation, TRECVID 2009
AU - Mühling, Markus
AU - Ewerth, Ralph
AU - Stadelmann, Thilo
AU - Shi, Bing
AU - Freisleben, Bernd
PY - 2009
Y1 - 2009
N2 - In this paper, we summarize our results for the high-level feature extraction task at TRECVID 2009. Our last year's high-level feature extraction system relied on low-level features as well as on state-of-theart approaches for camera motion estimation, text detection, face detection and audio segmentation. Based on the observation that the use of face detection results improved the performance of several face related concepts, we have incorporated further specialized object detectors. Using specialized object detectors trained on separate public data sets, objectbased features are generated by assembling detection results to object sequences. A shot-based confidence score and additional features, such as position, frame coverage and movement, are computed for each object class. The object detectors are used for two purposes: (a) to provide retrieval results for concepts directly related to the object class (such as using the boat detector for the concept boat), (b) to provide objectbased features as additional input for the SVM-based concept classifiers. Thus, other related concepts can also profit from object-based features. Furthermore, we investigated the use of SURF (Speeded Up Robust Features). The use of object-based features improved the high-level feature extraction results significantly. Our best run achieved a mean inferred average precision of 9.53%.
AB - In this paper, we summarize our results for the high-level feature extraction task at TRECVID 2009. Our last year's high-level feature extraction system relied on low-level features as well as on state-of-theart approaches for camera motion estimation, text detection, face detection and audio segmentation. Based on the observation that the use of face detection results improved the performance of several face related concepts, we have incorporated further specialized object detectors. Using specialized object detectors trained on separate public data sets, objectbased features are generated by assembling detection results to object sequences. A shot-based confidence score and additional features, such as position, frame coverage and movement, are computed for each object class. The object detectors are used for two purposes: (a) to provide retrieval results for concepts directly related to the object class (such as using the boat detector for the concept boat), (b) to provide objectbased features as additional input for the SVM-based concept classifiers. Thus, other related concepts can also profit from object-based features. Furthermore, we investigated the use of SURF (Speeded Up Robust Features). The use of object-based features improved the high-level feature extraction results significantly. Our best run achieved a mean inferred average precision of 9.53%.
UR - http://www.scopus.com/inward/record.url?scp=84905686331&partnerID=8YFLogxK
M3 - Paper
Y2 - 16 November 2009 through 17 November 2009
ER -