Efficient Classification of Images with Taxonomies

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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 efficient 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.
OriginalspracheEnglisch
TitelComputer 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)
Anzahl der Seiten12
ErscheinungsortBerlin, Heidelberg
VerlagSpringer Verlag
Erscheinungsdatum2010
Seiten351-362
ISBN (Print)978-3-642-12296-5
ISBN (elektronisch)978-3-642-12297-2
DOIs
PublikationsstatusErschienen - 2010
Extern publiziertJa
Veranstaltung9th Asian Conference on Computer Vision - ACCV 2009 - Xi'an, China
Dauer: 23.09.200927.09.2009
Konferenznummer: 9

DOI

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Forschende

  1. Marc-Philipp Dagott

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