Abstract
Though wavelet transform based methods have recently raised increasing interests in texture analysis due to their good space and frequency localization, many issues related to the choice of the wavelet basis and texture feature remain unresolved. In this paper, we evaluate the performance of seven wavelet energy signatures and eight wavelet basis for texture discrimination. Experimental results on 111 Brodatz textures show that the feature extracted from high and middle frequency channels is more suitable for texture analysis and the choice of wavelet basis has some influence on texture discrimination.
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References
Haralick, R.M., Shanmugam, K.K., Dinstein, L.: Features for Image Classification. IEEE Trans. Syst. Cyb. 8(6), 610–621 (1973)
Galloway, M.M.: Texture Analysis Using Gray Level Run Lengths. Comput. Graphics Image Process. 4, 172–179 (1975)
Ashjari, B.: Singular Value Decomposition Texture Measurement for Image Classification, PhD, thesis, University of Southern Califomia, Los Angeles, CA (1982)
Cross, G.R., Jain, A.K.: Markov Random Field Texture Models. IEEE Trans. Pattern Anal. Machine Intell. PAMI-5(1), 25–39 (1983)
Derin, H., Elliot, H.: Model and Segmentation of Noisy and Textured Images Using Gibbs random Fields. IEEE Trans. Pattern Anal. Machine Intell. PAMI-1, 251–259 (1987)
Unser, M.: Local Linear Transforms for Texture Measurements. Signal Process 11, 61–79 (1986)
Daugman, J.G.: An Information-theoretic View of Analog Representation in Striate Cortex. Comp. Neurosc., pp. 403–424 (1990)
Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel Texture Analysis Using Localized Spatial Filters. IEEE Trans. PAMI 12, 55–73 (1990)
Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Fillters. Pattern Recognition 24, 1167–1186 (1991)
Arivazhagan, S., Ganesan, L.: Texture Classification Using Wavelet Transform. Pattern Recogn. Lett. 24, 1513–1521 (2003)
Arivazhagan, S., Ganesan, L.: Texture Segmentation Using Wavelet Transform. Pattern Recogn. Lett. 24, 3197–3203 (2003)
Bharati, M.H., Liu, J.J., Macgregor, J.F.: Image Texture Analysis: Methods and Comparisons. Chemometrics and intelligent laboratory systems 72, 57–71 (2004)
Mor, E., Aladjem, M.: Boundary Refinements for Wavelet-domain Multiscale Texture Segmentation. Image and Vision Computing 23, 1150–1158 (2005)
Mallat, S.: A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. PAMI 11(7), 674–693 (1989)
Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)
Chang, T., Kuo, C.-C.J.: Texture Analysis and Classification with Tree-structured Wavelet Transform. IEEE Trans. Image Processing 2(4), 429–441 (1993)
Unser, M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Transactions on Image Processing 4(11), 1549–1560 (1995)
Mojsilović, A., Popović, M.V., Rackov, D.M.: On the Selection of an Optimal Wavelet Basis for Texture Characterization. IEEE Transactions on Image Processing 9(12), 2043–2050 (2000)
Brodatz, P.: Textures, a Photographic Album for Artists and Designers. Dover Publications, New York (1966)
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, X., Tian, Z. (2006). Wavelet Energy Signature: Comparison and Analysis. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_53
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DOI: https://doi.org/10.1007/11893257_53
Publisher Name: Springer, Berlin, Heidelberg
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