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, (Tam Metin Bildiri)

• Yayın Türü: Bildiri / Tam Metin Bildiri
• Doi Numarası: 10.1109/iscis.2008.4717904
• Basıldığı Şehir: İstanbul
• Basıldığı Ülke: Türkiye
• Sayfa Sayıları: ss.257-258
• Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
• Galatasaray Üniversitesi Adresli: Evet

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.