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An evaluation of divide-and-combine strategies for image categorization by multi-class support vector machines

2008 23rd International Symposium on Computer and Information Sciences, ISCIS 2008, Page: 1-6
2008
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Conference Paper Description

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. © 2008 IEEE.

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