Abstract
This paper presents a new, simple approach for rotation and histogram equalization invariant texture classification. The proposed approach is based on both microscopic and macroscopic information which can effectively capture fundamental intensity properties of image textures. The combined information is proven to be a very powerful texture feature. We extract the information at the microscopic level by using the frequency histogram of all pattern labels. At the macroscopic level, we extract the information by employing the circular Gabor filters at different center frequencies and computing the Tsallis entropy of the filter outputs. The proposed approach is robust in terms of histogram equalization since the feature is, by definition, invariant against flattening of pixel intensities. The good performance of this approach is proven by the promising experimental results obtained. We also evaluate our method based on six widely used image features. It is experimentally shown that our features exceed the performance obtained using other image features.
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Greenspan, H., Belongie, S., Goodman, R., Perona, P.: Rotation Invariant Texture Recognition Using a Steerable Pyramid. In: Proc. 12th IAPR ICPR, vol. 2, pp. 162–167 (1994)
Porter, R., Canagarajah, N.: Robust Rotation-Invariant Texture Classification: Wavelet, Gabor Filter and GMRF Based Schemes. In: IEE Proc. Conf. VISP, vol. 144(3), pp. 180–188 (1997)
Haley, G.M., Manjunath, B.S.: Rotation-Invariant Texture Classification Using a Complete Space-Frequency Model. IEEE Trans. Img. Proc. 8(2), 255–269 (1999)
Kashyap, R.L., Khotanzad, A.: A Model-Based Method for Rotation Invariant Texture Classification. IEEE Trans. PAMI 8(7), 472–481 (1986)
Deng, H., Clausi, D.A.: Gaussian MRF Rotation-Invariant Features for Image Classification. IEEE Trans. PAMI 26(7) (2004)
Arof, H., Deravi, F.: Circular Neighborhood and 1-D DFT Features for Texture Classification and Segmentation. In: IEE Proc. Conf. VISP, vol. 145(3), pp. 167–172 (1998)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. PAMI 24(7), 971–987 (2002)
Huang, X., Li, S.Z., Wang, Y.: Shape Localization Based on Statistical Method Using Extended Local Binary Pattern. In: IEEE Proc. Conf. Image and Graphics, pp. 184–187 (2004)
Sporring, J., Weickert, J.: Information Measures in Scale-Spaces. IEEE Trans. Information Theory 45(3), 1051–1058 (1999)
Fogel, I., Sagi, D.: Gabor Filters as Texture Discriminator. Biological Cybernetics 61, 103–113 (1989)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)
MeasTex Image Texture Database and Test Suite, Centre for Sensor Signal and Information Processing, the University of Queensland
Dana, K., Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and Texture of Real-World Surfaces. ACM Trans. Graphics 18(1), 1–34 (1999)
Tsallis, C.: Nonextensive Statistics: Theoretical, Experimental and Computational Evidences and Connections. Brazilian J. Physics 29(1) (1999)
Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel texture analysis using localised spatial filters. IEEE Trans. PAMI, 55–73 (1990)
Teuner, A., Pichler, O., Hosticka, B.J.: Unsupervised texture segmentation of images using tuned matched Gabor filters. IEEE Trans. Img. Proc., 863–870 (1995)
Laine, A., Fan, J.: Texture Classification by Wavelet Packet Signatures. IEEE Trans. PAMI 15(11) (1993)
Efstathios, H., Michael, D., Nayar, S.: Multiresolution Histograms and Their Use for Recognition. IEEE Trans. PAMI 26(7) (2004)
Chellappa, R., Chatterjee, S.: Classification of textures using Gaussian Markov random fields. IEEE Trans. ASSP, 959–963 (1985)
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© 2006 Springer-Verlag Berlin Heidelberg
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Liao, S., Law, W.K., Chung, A.C.S. (2006). Combining Microscopic and Macroscopic Information for Rotation and Histogram Equalization Invariant Texture Classification. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_11
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DOI: https://doi.org/10.1007/11612032_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
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