Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advanced Concepts for Intelligent Vision Systems |
| Untertitel | 11th International Conference, ACIVS 2009, Proceedings |
| Seiten | 253-264 |
| Seitenumfang | 12 |
| Publikationsstatus | Veröffentlicht - 2009 |
| Extern publiziert | Ja |
| Veranstaltung | 11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009 - Bordeaux, Frankreich Dauer: 28 Sept. 2009 → 2 Okt. 2009 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 5807 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
The temporal segmentation of a video into shots is a fundamental prerequisite for video retrieval. There are two types of shot boundaries: abrupt shot changes ("cuts") and gradual transitions. Several high-quality algorithms have been proposed for detecting cuts, but the successful detection of gradual transitions remains a surprisingly difficult problem in practice. In this paper, we present an unsupervised approach for detecting gradual transitions. It has several advantages. First, in contrast to alternative approaches, no training stage and hence no training data are required. Second, no thresholds are needed, since the used clustering approach separates classes of gradual transitions and non-transitions automatically and adaptively for each video. Third, it is a generic approach that does not employ a specialized detector for each transition type. Finally, the issue of removing false alarms caused by camera motion is addressed: in contrast to related approaches, it is not only based on low-level features, but on the results of an appropriate algorithm for camera motion estimation. Experimental results show that the proposed approach achieves very good performance on TRECVID shot boundary test data.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Advanced Concepts for Intelligent Vision Systems : 11th International Conference, ACIVS 2009, Proceedings. 2009. S. 253-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 5807 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Unsupervised detection of gradual video shot changes with motion-based false alarm removal
AU - Ewerth, Ralph
AU - Freisleben, Bernd
PY - 2009
Y1 - 2009
N2 - The temporal segmentation of a video into shots is a fundamental prerequisite for video retrieval. There are two types of shot boundaries: abrupt shot changes ("cuts") and gradual transitions. Several high-quality algorithms have been proposed for detecting cuts, but the successful detection of gradual transitions remains a surprisingly difficult problem in practice. In this paper, we present an unsupervised approach for detecting gradual transitions. It has several advantages. First, in contrast to alternative approaches, no training stage and hence no training data are required. Second, no thresholds are needed, since the used clustering approach separates classes of gradual transitions and non-transitions automatically and adaptively for each video. Third, it is a generic approach that does not employ a specialized detector for each transition type. Finally, the issue of removing false alarms caused by camera motion is addressed: in contrast to related approaches, it is not only based on low-level features, but on the results of an appropriate algorithm for camera motion estimation. Experimental results show that the proposed approach achieves very good performance on TRECVID shot boundary test data.
AB - The temporal segmentation of a video into shots is a fundamental prerequisite for video retrieval. There are two types of shot boundaries: abrupt shot changes ("cuts") and gradual transitions. Several high-quality algorithms have been proposed for detecting cuts, but the successful detection of gradual transitions remains a surprisingly difficult problem in practice. In this paper, we present an unsupervised approach for detecting gradual transitions. It has several advantages. First, in contrast to alternative approaches, no training stage and hence no training data are required. Second, no thresholds are needed, since the used clustering approach separates classes of gradual transitions and non-transitions automatically and adaptively for each video. Third, it is a generic approach that does not employ a specialized detector for each transition type. Finally, the issue of removing false alarms caused by camera motion is addressed: in contrast to related approaches, it is not only based on low-level features, but on the results of an appropriate algorithm for camera motion estimation. Experimental results show that the proposed approach achieves very good performance on TRECVID shot boundary test data.
KW - Gradual transition detection
KW - Gradual video shot changes
KW - Shot boundary detection
KW - Video indexing
KW - Video retrieval
UR - http://www.scopus.com/inward/record.url?scp=70549108094&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04697-1_24
DO - 10.1007/978-3-642-04697-1_24
M3 - Conference contribution
AN - SCOPUS:70549108094
SN - 3642046967
SN - 9783642046964
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 253
EP - 264
BT - Advanced Concepts for Intelligent Vision Systems
T2 - 11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009
Y2 - 28 September 2009 through 2 October 2009
ER -