Comparison of multiclass SVM decomposition schemes for visual object recognition

Authors:
Laine Kahsay;Friedhelm Schwenker;Günther Palm
Affiliations:
Department of Neural Information Processing, University of Ulm, Ulm;Department of Neural Information Processing, University of Ulm, Ulm;Department of Neural Information Processing, University of Ulm, Ulm
Venue:
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Year:
2005

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Abstract

We consider the problem of multiclass decomposition schemes for Support Vector Machines with Linear, Polynomial and RBF kernels. Our aim is to compare and discuss popular multiclass decomposing approaches such as the One versus the Rest, One versus One, Decision Directed Acyclic Graphs, Tree Structured, Error Correcting Output Codes. We conducted our experiments on benchmark datastes consisting of camera images of 3D objects. In our experiments we found that all the multiclass decomposing schemes for SVMs performed comparably very well with no significant statistical differences in cases of nonlinear kernels. In case of linear kernels the multiclass schemes OvR, OvO and DDAG outperform Tree Structured and ECOC.