Abstract
The performance of Support Vector Machines (SVMs) is highly dependent on the choice of a kernel function suited to the problem at hand. In particular, the kernel implicitly performs a feature selection which is the most important stage in any texture classification algorithm. In this work a new Gabor filter based kernel for texture classification with SVMs is proposed. The proposed kernel function is based on a Gabor filter decomposition and exploiting linear predictive coding (LPC) in each subband, and exploiting a filter selection method to choose the best filters. The proposed texture classification method is evaluated using several texture samples, and compared with recently published methods. The comprehensive evaluation of the proposed method shows significant improvement in classification error rate.
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References
Marti, J., Batlle, J., Casals, A.: Model-based objects recognition in industrial environments. In: Proc. ICRA, IEEE, Los Alamitos (1997)
Horng, M.H., Sun, Y.N., Lin, X.Z.: Texture feature coding for classification of liver. Computerized Medical Imaging and Graphics 26, 33–42 (2002)
Kim, J., Park, H.: Statistical texture features for detection of microcalcifications. IEEE Transaction on Medical Imaging 18, 231–238 (1999)
Scholkopf, B., Sung, K., Burges, C.J.C., Girosi, F., Niyogi, P., Pogio, T., Vapnik, V.: Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Transaction on Signal Processing 45, 2765–2785 (1997)
Kim, K.I., Jung, K., Park, S.H., Kim, H.J.: Support vector machine for texture classification. IEEE Transaction on PAMI 24, 1542–1550 (2002)
Rabiner, L., Juang, B.H.: Fundamentals of speech recognition. Printce Hall, Englewood Cliffs (1993)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture in two nonstriate visual areas 18 and 19 of the cat. J. Neurophysiol. 28, 229–289 (1965)
Clausi, D.A., Jerningan, M.E.: Designing gabor filters for optimal texture seprability. Pattern Recognition Letters 33, 1835–1849 (2000)
Davy, M., Doncarli, C.: A new non-stationary test procedure for improved loud speaker fault detection. J. Audio Eng. Soc. 50, 458–469 (2002)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2000)
Brodatz, P.: Textures Album for Artists and Designers. New york (1966)
MIT Vision and Modeling Group (1998)
Manian, V., Vasquez, R., Katiyar, P.: Texture classification using logical operators. IEEE Transaction on Image Processing 9, 1693–1703 (2000)
Arivazhagan, S., Ganesan, L.: Texture classification using wavelet transform. Pattern Recognition Letters 24, 1513–1521 (2003)
Randen, T., Husoy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. on Pattern Recognit. Machine Intell. 21, 291–310 (1999)
Liu, X., Wang, D.: Texture classification using spectral histogram. IEEE Trans. Image Processing 12, 661–670
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Sabri, M., Fieguth, P. (2004). A New Gabor Filter Based Kernel for Texture Classification with SVM. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_39
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DOI: https://doi.org/10.1007/978-3-540-30126-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
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