An Evaluation of Divide-and-Combine Strategies for Image Categorization by Multi-Class Support Vector Machines

Creative Commons License

Demirkesen C., Cherifi H.

23rd International Symposium on Computer and Information Sciences (ISCIS), İstanbul, Turkey, 27 - 29 October 2008, pp.257-258 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/iscis.2008.4717904
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.257-258


Categorization of real world images without human intervention is a challenging ongoing research. The nature of this problem requires usage of multiclass classification techniques. In divide-and-combine approach, a multiclass problem is divided into a set of binary classification problems and then the binary classifications are combined to obtain multi-class classification. Our objective in this work is to compare several divide-and-combine multiclass SVM classification strategies for real world image classification. Our results show that One-against-all and One-against-one MaxWins are the most efficient methods.