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