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Communication Dans Un Congrès Année : 2008

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

Can Demirkesen
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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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Dates et versions

hal-00612239 , version 1 (28-07-2011)

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Citer

Can Demirkesen, Hocine Cherifi. An Evaluation of Divide-and-Combine Strategies for Image Categorization by Multi-Class Support Vector Machines. 23rd International Symposium on Computer and Information Sciences, 2008. ISCIS '08, Oct 2008, Istanbul, Turkey. pp.1 - 6, ⟨10.1109/ISCIS.2008.4717904⟩. ⟨hal-00612239⟩
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