Details
| Original language | English |
|---|---|
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | TREC Video Retrieval Evaluation, TRECVID 2007 - Gaithersburg, MD, United States Duration: 5 Nov 2007 → 6 Nov 2007 |
Conference
| Conference | TREC Video Retrieval Evaluation, TRECVID 2007 |
|---|---|
| Country/Territory | United States |
| City | Gaithersburg, MD |
| Period | 5 Nov 2007 → 6 Nov 2007 |
Abstract
In this paper, we summarize our results for the shot boundary and high level feature detection task at TRECVID 2007. Our shot boundary detection approach of previous TRECVID evaluations served as a basis for our experiments this year and was modified in several ways. First, we have incorporated a new metric selection for cut detection based on the evaluation of a clustering result. Second, we have tested the possibility to improve cut detection results via self-supervised learning. Third, the unsupervised approach for gradual transition detection has been supplemented with a false alarm removal method using a state-of-the art camera motion estimation approach. Regarding high-level feature detection, one focus of this year's task was to investigate the question how well a trained system generalizes from the TRECVID 2005 news data to this year's Sound and Vision data. However, only two institutes have submitted four runs of the related type "a" for evaluation (three of them were submitted by us). In this paper, we present our experiments for the high-level feature task with respect to the generalization capabilities of our system trained on broadcast news videos. For this purpose, we have conducted several experiments using our system which is based on low-level features as well as on state-ofthe- art approaches for camera motion estimation, text detection, face detection and audio segmentation.
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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2007. Paper presented at TREC Video Retrieval Evaluation, TRECVID 2007, Gaithersburg, MD, United States.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - University of Marburg at TRECVID 2007
T2 - TREC Video Retrieval Evaluation, TRECVID 2007
AU - Mühling, Markus
AU - Ewerth, Ralph
AU - Stadelmann, Thilo
AU - Zöfel, Christian
AU - Shi, Bing
AU - Freisleben, Bernd
PY - 2007
Y1 - 2007
N2 - In this paper, we summarize our results for the shot boundary and high level feature detection task at TRECVID 2007. Our shot boundary detection approach of previous TRECVID evaluations served as a basis for our experiments this year and was modified in several ways. First, we have incorporated a new metric selection for cut detection based on the evaluation of a clustering result. Second, we have tested the possibility to improve cut detection results via self-supervised learning. Third, the unsupervised approach for gradual transition detection has been supplemented with a false alarm removal method using a state-of-the art camera motion estimation approach. Regarding high-level feature detection, one focus of this year's task was to investigate the question how well a trained system generalizes from the TRECVID 2005 news data to this year's Sound and Vision data. However, only two institutes have submitted four runs of the related type "a" for evaluation (three of them were submitted by us). In this paper, we present our experiments for the high-level feature task with respect to the generalization capabilities of our system trained on broadcast news videos. For this purpose, we have conducted several experiments using our system which is based on low-level features as well as on state-ofthe- art approaches for camera motion estimation, text detection, face detection and audio segmentation.
AB - In this paper, we summarize our results for the shot boundary and high level feature detection task at TRECVID 2007. Our shot boundary detection approach of previous TRECVID evaluations served as a basis for our experiments this year and was modified in several ways. First, we have incorporated a new metric selection for cut detection based on the evaluation of a clustering result. Second, we have tested the possibility to improve cut detection results via self-supervised learning. Third, the unsupervised approach for gradual transition detection has been supplemented with a false alarm removal method using a state-of-the art camera motion estimation approach. Regarding high-level feature detection, one focus of this year's task was to investigate the question how well a trained system generalizes from the TRECVID 2005 news data to this year's Sound and Vision data. However, only two institutes have submitted four runs of the related type "a" for evaluation (three of them were submitted by us). In this paper, we present our experiments for the high-level feature task with respect to the generalization capabilities of our system trained on broadcast news videos. For this purpose, we have conducted several experiments using our system which is based on low-level features as well as on state-ofthe- art approaches for camera motion estimation, text detection, face detection and audio segmentation.
UR - http://www.scopus.com/inward/record.url?scp=84905159151&partnerID=8YFLogxK
M3 - Paper
Y2 - 5 November 2007 through 6 November 2007
ER -