Details
| Original language | English |
|---|---|
| Title of host publication | Computer Analysis of Images and Patterns |
| Subtitle of host publication | 14th International Conference, CAIP 2011, Proceedings |
| Pages | 386-393 |
| Number of pages | 8 |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 - Seville, Spain Duration: 29 Aug 2011 → 31 Aug 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 1 |
| Volume | 6854 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 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.
Keywords
- Hough forests, Multi-class object detection, object recognition
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 : 14th International Conference, CAIP 2011, Proceedings. PART 1. ed. 2011. p. 386-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6854 LNCS, No. PART 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -