Paper Title:
Wavelets-Based Feature Extraction for Texture Classification
  Abstract

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

  Info
Periodical
Advanced Materials Research (Volumes 97-101)
Edited by
Zhengyi Jiang and Chunliang Zhang
Pages
1273-1276
DOI
10.4028/www.scientific.net/AMR.97-101.1273
Citation
G. Yu, Y. Z. Lin, S. Kamarthi, "Wavelets-Based Feature Extraction for Texture Classification", Advanced Materials Research, Vols. 97-101, pp. 1273-1276, 2010
Online since
March 2010
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Price
$32.00
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