A Comparison of Multiclass SVM Methods for Real World Natural Scenes

Demirkesen C., Cherifi H.

10th International Conference on Advanced Concepts for Intelligent Vision Systems, Juan les Pins, Fransa, 20 - 24 Ekim 2008, cilt.5259, ss.752-763 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 5259
  • Doi Numarası: 10.1007/978-3-540-88458-3-68
  • Basıldığı Şehir: Juan les Pins
  • Basıldığı Ülke: Fransa
  • Sayfa Sayıları: ss.752-763


Categorization of natural scene images into semantically meaningful categories is a challenging problem that requires usage of multiclass classification methods. Our objective in this work is to compare multiclass SVM classification strategies for this task. We compare the approaches where a multi-class classifier is constructed by combining several binary classifiers and the approaches that consider all classes at once. The first approach is generally termed as "divide-and-combine" and the second is known as "all-in-one". Our experimental results show that all-in-one SVM outperforms the other methods.