Abstract
Texture analysis is used in numerous applications in various fields. There have been many different approaches/techniques in the literature for texture analysis among which the texton-based approach that computes the primitive elements representing textures using k-means algorithm has shown great success. Recently, dictionary learning and sparse coding has provided state-of-the-art results in various applications. With recent advances in computing the dictionary and sparse coefficients using fast algorithms, it is possible to use these techniques to learn the primitive elements and histogram of them to represent textures. In this paper, online learning is used as fast implementation of sparse coding for texture classification. The results show similar to or better performance than texton based approach on CUReT database despite of computation of dictionary without taking into account the class labels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Petrou, M., Sevilla, P.G.: Image Processing Dealing with Texture. John Wiley and Sons, West Sussex (2006)
Ahonen, T., Pietikainen, M.: Image Description Using Joint Distribution of Filter Bank Responses. Pattern Recognition Letters 30(4), 368–376 (2009)
Mirmehdi, M., Xie, X., Suri, J.: Handbook of Texture Analysis. Imperial Collage Press, London (2008)
Hadjidemetriou, E., Grossberg, M.D., Nayar, S.K.: Multiresolution Histograms and Their Use for Recognition. IEEE Trans. on PAMI 26(7), 831–847 (2004)
Julesz, B.: Textons, the Elements of Texture Perception, and Their Interactions. Nature 290(5802), 91–97 (1981)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. on PAMI 24(7), 971–987 (2002)
Leung, T., Malik, J.: Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons. Int’l J. Computer Vision 43(1), 29–44 (2001)
Schmid, C.: Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval. International Journal of Computer Vision 56(1/2), 7–16 (2004)
Cula, O.G., Dana, K.J.: 3D Texture Recognition Using Bidirectional Feature Histograms. International Journal of Computer Vision 59(1), 33–60 (2004)
Varma, M., Zisserman, A.: A Statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision: Special Issue on Texture Analysis and Synthesis 62(1-2), 61–81 (2005)
Varma, M., Zisserman, A.: A Statistical Approach to Material Classification Using Image Patch Exemplars. IEEE Trans. on PAMI 31(11), 2032–2047 (2009)
Marial, J., Bach, F., Ponce, J., Sapiro, G.: Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research 11, 19–60 (2010)
Biggs, M., Ghodsi, A., Vavasis, S.: Nonnagative Matrix Factorization via Rank-One Downdate. In: Int’l Conf. on Machine Learning (ICML), Helsinki, Finland, pp. 64–71 (2008)
Friedman, J., Hastie, T., Tibshirani, R.: Regularized Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33(1), 1–22 (2010)
Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and Texture of Real-World Surfaces. ACM Transactions on Graphics 18(1), 1–34 (1999)
Olshausen, B.A., Field, D.J.: Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images. Nature 381, 607–609 (1996)
Olshausen, B.A., Field, D.J.: Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1? Vision Research 37(23), 3311–3325 (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley and Sons, New York (2001)
Marial, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised Dictionary Learning. In: 22nd Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, pp. 1033–1040 (2008)
Mallat, S.: Wavelet Tour of Signal Processing: The Sparse Way, 3rd edn. Academic Press, Burlington (2009)
Tibshirani, R.: Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society, Series B 58(1), 267–288 (1996)
Xie, J., Zhang, L., You, J., Zhang, D.: Texture Classification via Patch-Based Sparse Texton Learning. In: Int’l Conf. on Image Processing (ICIP), Hong Kong, pp. 2737–2740 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gangeh, M.J., Ghodsi, A., Kamel, M.S. (2011). Dictionary Learning in Texture Classification. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-21593-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21592-6
Online ISBN: 978-3-642-21593-3
eBook Packages: Computer ScienceComputer Science (R0)