On taxonomies for multi-class image categorization

Alexander Binder, Klaus Robert Müller, Motoaki Kawanabe

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


We study the problem of classifying images into a given, pre-determined taxonomy. This task can be elegantly translated into the structured learning framework. However, despite its power, structured learning has known limits in scalability due to its high memory requirements and slow training process. We propose an efficient approximation of the structured learning approach by an ensemble of local support vector machines (SVMs) that can be trained efficiently with standard techniques. A first theoretical discussion and experiments on toy-data allow to shed light onto why taxonomy-based classification can outperform taxonomy-free approaches and why an appropriately combined ensemble of local SVMs might be of high practical use. Further empirical results on subsets of Caltech256 and VOC2006 data indeed show that our local SVM formulation can effectively exploit the taxonomy structure and thus outperforms standard multi-class classification algorithms while it achieves on par results with taxonomy-based structured algorithms at a significantly decreased computing time.

Original languageEnglish
Pages (from-to)281-301
Number of pages21
JournalInternational Journal of Computer Vision
Issue number3
Publication statusPublished - 2012 Sep


  • Multi-class object categorization
  • Structure learning
  • Support vector machine
  • Taxonomies

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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