Texture Classification by Combining Wavelet and Contourlet Features

  • Shutao Li
  • John Shawe-Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

In the recent decades, many features used to represent a texture were proposed. However, these features are always used exclusively. In this paper, a novel approach is presented that combines two types of features extracted by discrete wavelet transform and contourlet transform. Support vector machines (SVMs), which have demonstrated excellent performance in a variety of pattern recognition problems, are used as classifiers. The algorithm is tested on four different datasets, selected from Brodatz and VisTex database. The experimental results show that the combined features result in better classification rates than using only one type of those.

Keywords

Support Vector Machine Filter Bank Texture Classification Gabor Filter Combine Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Shutao Li
    • 1
    • 2
  • John Shawe-Taylor
    • 2
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaP.R. China
  2. 2.ISIS Research Group, School of Electronics and Computer ScienceUniversity of SouthamptonUK

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