Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Computer Analysis of Images and Patterns |
| Untertitel | 14th International Conference, CAIP 2011, Proceedings |
| Seiten | 386-393 |
| Seitenumfang | 8 |
| Auflage | PART 1 |
| Publikationsstatus | Veröffentlicht - 2011 |
| Extern publiziert | Ja |
| Veranstaltung | 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 - Seville, Spanien Dauer: 29 Aug. 2011 → 31 Aug. 2011 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Nummer | PART 1 |
| Band | 6854 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Multi-class object detection is a promising approach for reducing the processing time of object recognition tasks. Recently, random Hough forests have been successfully used for single-class object detection. In this paper, we present an extension of random Hough forests for the purpose of multi-class object detection and propose local histograms of visual words as appropriate features. Experimental results for the Caltech-101 test set demonstrate that the performance of the proposed approach is almost as good as the performance of a single-class object detector, even when detecting a large number of 24 object classes at a time.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Computer Analysis of Images and Patterns : 14th International Conference, CAIP 2011, Proceedings. PART 1. Aufl. 2011. S. 386-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 6854 LNCS, Nr. PART 1).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Multi-class object detection with Hough forests using local histograms of visual words
AU - Mühling, Markus
AU - Ewerth, Ralph
AU - Shi, Bing
AU - Freisleben, Bernd
N1 - Funding Information: Acknowledgements. This work is supported by the German Ministry of Education and Research (BMBF, D-Grid) and by the German Research Foundation (DFG, PAK 509).
PY - 2011
Y1 - 2011
N2 - Multi-class object detection is a promising approach for reducing the processing time of object recognition tasks. Recently, random Hough forests have been successfully used for single-class object detection. In this paper, we present an extension of random Hough forests for the purpose of multi-class object detection and propose local histograms of visual words as appropriate features. Experimental results for the Caltech-101 test set demonstrate that the performance of the proposed approach is almost as good as the performance of a single-class object detector, even when detecting a large number of 24 object classes at a time.
AB - Multi-class object detection is a promising approach for reducing the processing time of object recognition tasks. Recently, random Hough forests have been successfully used for single-class object detection. In this paper, we present an extension of random Hough forests for the purpose of multi-class object detection and propose local histograms of visual words as appropriate features. Experimental results for the Caltech-101 test set demonstrate that the performance of the proposed approach is almost as good as the performance of a single-class object detector, even when detecting a large number of 24 object classes at a time.
KW - Hough forests
KW - Multi-class object detection
KW - object recognition
UR - http://www.scopus.com/inward/record.url?scp=80052811925&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23672-3_47
DO - 10.1007/978-3-642-23672-3_47
M3 - Conference contribution
AN - SCOPUS:80052811925
SN - 9783642236716
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 386
EP - 393
BT - Computer Analysis of Images and Patterns
T2 - 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011
Y2 - 29 August 2011 through 31 August 2011
ER -