Wavelets-Based Feature Extraction for Texture Classification


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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.



Advanced Materials Research (Volumes 97-101)

Edited by:

Zhengyi Jiang and Chunliang Zhang




G. Yu et al., "Wavelets-Based Feature Extraction for Texture Classification", Advanced Materials Research, Vols. 97-101, pp. 1273-1276, 2010

Online since:

March 2010




[1] S. G. Mallat: A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp.674-693 (1989).

DOI: https://doi.org/10.1109/34.192463

[2] P. H. Carter: Texture discrimination using wavelets, SPIE applications of digital image processing XIV, vol. 1567, pp.432-438 (1991).

DOI: https://doi.org/10.1117/12.50836

[3] A. Laine, and J. Fan: Texture classification by wavelet packet signatures, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp.1186-1191 (1993).

DOI: https://doi.org/10.1109/34.244679

[4] T. Chang and C. -C. J. Kuo: Texture analysis and classification with tree-structured wavelet transform, IEEE Trans. Image Processing, vol. 2, no. 4, pp.429-441 (1993).

DOI: https://doi.org/10.1109/83.242353

[5] N-D. Kim and S. Udpa: Texture classification using rotated wavelet filters, IEEE Trans. Syst., Man, Cybern. A, vol. 30, no. 6, pp.847-852 (2000).

DOI: https://doi.org/10.1109/3468.895915

[6] N. Porter and N. Canagarajah: Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes, IEE Proc. Vis. Image Signal Process., vol. 144, no. 3, pp.180-188 (1997).

DOI: https://doi.org/10.1049/ip-vis:19971182

[7] C. -M. Pun and M. -C. Lee: Rotation-invariant texture classification using a two-stage wavelet packet feature approach, IEE Proc. Vis. Image Signal Process., vol. 148, no. 6, pp.422-428 (2001).

DOI: https://doi.org/10.1049/ip-vis:20010705

[8] G. Yu, S. V. Kamarthi, and S. Pittner: A new wavelet-based clustering scheme and its applications, Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence.

[9] P. Brodatz: Textures: A Photographic Album for Artists and Designers. New York: Dover (1966).

[10] D. F. Specht: Probabilistic neural network, , eural , etworks, vol. 3, pp.109-118 (1990). Fig. 2. Cluster boundary formation using the proposed clustering procedure.

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