Comparison and fusion of multiresolution features for texture classification

Authors:
Shutao Li;John Shawe-Taylor
Affiliations:
College of Electrical and Information Engineering, Hunan University, Changsha 410082, PR China and Image, Speech and Intelligent Systems Research Group, School of Electronics and Computer Science, ...;Image, Speech and Intelligent Systems Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

In this paper, we investigate the texture classification problem with individual and combined multiresolution features, i.e., dyadic wavelet, wavelet frame, Gabor wavelet, and steerable pyramid. Support vector machines are used as classifiers. The experimental results show that the steerable pyramid and Gabor wavelet classify texture images with the highest accuracy, the wavelet frame follows them, the dyadic wavelet significantly lags behind. Experimental results on fused features demonstrated the combination of two feature sets always outperformed each method individually. And the fused feature sets of multi-orientation decompositions and stationary wavelet achieve the highest accuracy.