Standard
Efficient Classification of Images with Taxonomies. / Binder, Alexander; Kawanabe, Motoaki
; Brefeld, Ulf.
Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Berlin, Heidelberg: Springer, 2010. p. 351-362 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5996 LNCS, No. PART 3).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Binder, A, Kawanabe, M
& Brefeld, U 2010,
Efficient Classification of Images with Taxonomies. in
Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 5996 LNCS, Springer, Berlin, Heidelberg, pp. 351-362, 9th Asian Conference on Computer Vision - ACCV 2009, Xi'an, China,
23.09.09.
https://doi.org/10.1007/978-3-642-12297-2_34APA
Binder, A., Kawanabe, M.
, & Brefeld, U. (2010).
Efficient Classification of Images with Taxonomies. In
Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 351-362). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5996 LNCS, No. PART 3). Springer.
https://doi.org/10.1007/978-3-642-12297-2_34Vancouver
Binder A, Kawanabe M
, Brefeld U.
Efficient Classification of Images with Taxonomies. In Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Berlin, Heidelberg: Springer. 2010. p. 351-362. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-12297-2_34
Bibtex
@inbook{3693a99774594cf687f983f1706018a3,
title = "Efficient Classification of Images with Taxonomies",
abstract = "We study the problem of classifying images into a given, pre-determined taxonomy. The task can be elegantly translated into the structured learning framework. Structured learning, however, is known for its memory consuming and slow training processes. The contribution of our paper is twofold: Firstly, we propose an e.cient decomposition of the structured learning approach into an equivalent ensemble of local support vector machines (SVMs) which can be trained with standard techniques. Secondly, we combine the local SVMs to a global model by re-incorporating the taxonomy into the training process. Our empirical results on Caltech256 and VOC2006 data show that our local-global SVM effectively exploits the structure of the taxonomy and outperforms multi-class classification approaches.",
keywords = "Informatics, Empirical results, Global models, Local support, Local-global, Multi-class classification, Structured learning, Training process, Business informatics",
author = "Alexander Binder and Motoaki Kawanabe and Ulf Brefeld",
year = "2010",
doi = "10.1007/978-3-642-12297-2_34",
language = "English",
isbn = "978-3-642-12296-5",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
number = "PART 3",
pages = "351--362",
booktitle = "Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers",
address = "Germany",
note = "9th Asian Conference on Computer Vision - ACCV 2009, ACCV ; Conference date: 23-09-2009 Through 27-09-2009",
}
RIS
TY - CHAP
T1 - Efficient Classification of Images with Taxonomies
AU - Binder, Alexander
AU - Kawanabe, Motoaki
AU - Brefeld, Ulf
N1 - Conference code: 9
PY - 2010
Y1 - 2010
N2 - We study the problem of classifying images into a given, pre-determined taxonomy. The task can be elegantly translated into the structured learning framework. Structured learning, however, is known for its memory consuming and slow training processes. The contribution of our paper is twofold: Firstly, we propose an e.cient decomposition of the structured learning approach into an equivalent ensemble of local support vector machines (SVMs) which can be trained with standard techniques. Secondly, we combine the local SVMs to a global model by re-incorporating the taxonomy into the training process. Our empirical results on Caltech256 and VOC2006 data show that our local-global SVM effectively exploits the structure of the taxonomy and outperforms multi-class classification approaches.
AB - We study the problem of classifying images into a given, pre-determined taxonomy. The task can be elegantly translated into the structured learning framework. Structured learning, however, is known for its memory consuming and slow training processes. The contribution of our paper is twofold: Firstly, we propose an e.cient decomposition of the structured learning approach into an equivalent ensemble of local support vector machines (SVMs) which can be trained with standard techniques. Secondly, we combine the local SVMs to a global model by re-incorporating the taxonomy into the training process. Our empirical results on Caltech256 and VOC2006 data show that our local-global SVM effectively exploits the structure of the taxonomy and outperforms multi-class classification approaches.
KW - Informatics
KW - Empirical results
KW - Global models
KW - Local support
KW - Local-global
KW - Multi-class classification
KW - Structured learning
KW - Training process
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=78650463536&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12297-2_34
DO - 10.1007/978-3-642-12297-2_34
M3 - Article in conference proceedings
SN - 978-3-642-12296-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 351
EP - 362
BT - Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
PB - Springer
CY - Berlin, Heidelberg
T2 - 9th Asian Conference on Computer Vision - ACCV 2009
Y2 - 23 September 2009 through 27 September 2009
ER -
