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


Demirkesen C., Cherifi H.

23rd International Symposium on Computer and Information Sciences (ISCIS), İstanbul, Türkiye, 27 - 29 Ekim 2008, ss.257-258 identifier identifier

  • Doi Numarası: 10.1109/iscis.2008.4717904
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.257-258

Özet

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.