Abstract
Sparse coding theory is a method for finding a reduced representation of multidimensional data. When applied to images, this theory can adopt efficient codes for images that captures the statistically significant structure intrinsic in the images. In this paper, we mainly discuss about its application in the area of texture images analysis by means of Independent Component Analysis. Texture model construction, feature extraction and further segmentation approaches are proposed respectively. The experimental results demonstrate that the segmentation based on sparse coding theory gets promising performance.
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.
Similar content being viewed by others
References
Reed, T.R., Hans du Buf, J.M.: A review of recent texture segmentation and feature extraction techniques. CVGIP: Image Understanding 57(3), 359–372 (1993)
Tuceryan, M., Jain, A.K.: Texture Analysis. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific Publishing Company, Singapore (1998)
Van Hateren, J.K., Van der Schaaf, A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London, B225 265, 359–366 (1998)
Simoncelli, E.P.: Vision and the statistics of the visual environment. Current opinion in Neurobiology 13(2), 144–149 (2003)
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)
Hyvarinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. In: Haykin, S. (series ed.) Communications, and Control. Wiley Series on Adaptive and Learning Systems for Signal Processing, pp. 165–237. John Wiley and Sons, Inc., Chichester (2001)
Peyre, G.: Non-negative sparse modeling of textures. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 628–639. Springer, Heidelberg (2007)
Hyvarinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Network 10(3), 626–634 (1999)
Demster, A.P., Laird, N.M., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B. 39, 1–38 (1977)
Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognition 24(12), 1167–1186 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duan, L., Ma, J., Yang, Z., Miao, J. (2010). An Approach to Texture Segmentation Analysis Based on Sparse Coding Model and EM Algorithm. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)