Multiresolution histograms for SVM-Based texture classification

Authors:
Srinivas Andra;Yongjun Wu
Affiliations:
ECSE Department, Rensselaer Polytechnic Institute, Troy, NY;ECSE Department, Rensselaer Polytechnic Institute, Troy, NY
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

Citing 5
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Abstract

Multiresolution histograms have been recently proposed as robust and efficient features for texture classification. In this paper, we evaluate the performance of multiresolution histograms for texture classification using support vector machines (SVMs). We observe that the dimensionality of multiresolution histograms can be greatly reduced with a Laplacian pyramidal decomposition. With an appropriate kernel, we show that SVMs significantly improve the performance of multiresolution histograms compared to the previously used nearest-neighbor (NN) classifiers on a texture classification problem involving Brodatz textures. Experimental results indicate that multiresolution histograms in conjunction with SVMs are also robust to noise.