A Comparison of Multiclass SVM Methods for Real World Natural Scenes

Authors:
Can Demirkesen;Hocine Cherifi
Affiliations:
Institue of Science and Engineering, Galatasaray University, Ortakoy, Turkey 34257;Institue of Science and Engineering, Galatasaray University, Ortakoy, Turkey 34257 and Faculté des Science Mirande 9, Dijon, France 21078
Venue:
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Year:
2008

Citing 5
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Pairwise classification and support vector machines

Advances in kernel methods
Applying multi-class SVMs into scene image classification

IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Categorization of natural scenes: local vs. global information

APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Geometry aware local kernels for object recognition

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
A New Image Distortion Measure Based on Natural Scene Statistics Modeling

International Journal of Computer Vision and Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Categorization of natural scene images into semantically meaningful categories is a challenging problem that requires usage of multiclass classification methods. Our objective in this work is to compare multiclass SVM classification strategies for this task. We compare the approaches where a multi-class classifier is constructed by combining several binary classifiers and the approaches that consider all classes at once. The first approach is generally termed as "divide-and-combine" and the second is known as "all-in-one". Our experimental results show that all-in-one SVM outperforms the other methods.