Details
| Original language | English |
|---|---|
| Title of host publication | Computer Analysis of Images and Patterns |
| Subtitle of host publication | 13th International Conference, CAIP 2009, Proceedings |
| Pages | 1036-1043 |
| Number of pages | 8 |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009 - Munster, Germany Duration: 2 Sept 2009 → 4 Sept 2009 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5702 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Recently, performance prediction has been successfully applied in the field of information retrieval for content analysis and retrieval tasks. This paper discusses how performance prediction can be realized for unsupervised learning approaches in the context of video content analysis and indexing. Performance prediction helps in identifying the number of detection errors and can thus support post-processing. This is demonstrated for the example of temporal video segmentation by presenting an approach for automatically predicting the precision and recall of a video cut detection result. It is shown for the unsupervised cut detection approach that the related clustering validity measure is highly correlated with the precision of a detection result. Three regression methods are investigated to exploit the observed correlation. Experimental results demonstrate the feasibility of the proposed performance prediction approach.
Keywords
- Performance prediction, Video indexing, Video retrieval
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Computer Analysis of Images and Patterns : 13th International Conference, CAIP 2009, Proceedings. 2009. p. 1036-1043 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5702 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Performance prediction for unsupervised video indexing
AU - Ewerth, Ralph
AU - Freisleben, Bernd
PY - 2009
Y1 - 2009
N2 - Recently, performance prediction has been successfully applied in the field of information retrieval for content analysis and retrieval tasks. This paper discusses how performance prediction can be realized for unsupervised learning approaches in the context of video content analysis and indexing. Performance prediction helps in identifying the number of detection errors and can thus support post-processing. This is demonstrated for the example of temporal video segmentation by presenting an approach for automatically predicting the precision and recall of a video cut detection result. It is shown for the unsupervised cut detection approach that the related clustering validity measure is highly correlated with the precision of a detection result. Three regression methods are investigated to exploit the observed correlation. Experimental results demonstrate the feasibility of the proposed performance prediction approach.
AB - Recently, performance prediction has been successfully applied in the field of information retrieval for content analysis and retrieval tasks. This paper discusses how performance prediction can be realized for unsupervised learning approaches in the context of video content analysis and indexing. Performance prediction helps in identifying the number of detection errors and can thus support post-processing. This is demonstrated for the example of temporal video segmentation by presenting an approach for automatically predicting the precision and recall of a video cut detection result. It is shown for the unsupervised cut detection approach that the related clustering validity measure is highly correlated with the precision of a detection result. Three regression methods are investigated to exploit the observed correlation. Experimental results demonstrate the feasibility of the proposed performance prediction approach.
KW - Performance prediction
KW - Video indexing
KW - Video retrieval
UR - http://www.scopus.com/inward/record.url?scp=70349305548&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03767-2_126
DO - 10.1007/978-3-642-03767-2_126
M3 - Conference contribution
AN - SCOPUS:70349305548
SN - 3642037666
SN - 9783642037665
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1036
EP - 1043
BT - Computer Analysis of Images and Patterns
T2 - 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
Y2 - 2 September 2009 through 4 September 2009
ER -