Abstract
An efficient and robust type of unsupervised multispectral texture segmentation method is presented. Single decorrelated monospectral texture factors are assumed to be represented by a set of local Gaussian Markov random field (GMRF) models evaluated for each pixel centered image window and for each spectral band. The segmentation algorithm based on the underlying Gaussian mixture (GM) model operates in the decorrelated GMRF parametric space. The algorithm starts with an oversegmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached.
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References
Reed, T.R., du Buf, J.M.H.: A review of recent texture segmentation and feature extraction techniques. CVGIP–Image Understanding 57, 359–372 (1993)
Kashyap, R.: Image models. In: Young, T.Y. (ed.) Handbook of Pattern Recognition and Image Processing, Academic Press, New York (1986)
Haindl, M.: Texture synthesis. CWI Quarterly 4, 305–331 (1991)
Mao, A.J.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition 25, 173–188 (1992)
Panjwani, G.H.: Markov random field models for unsupervised segmentation of textured color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 939–954 (1995)
Munjah, R.C.: Unsupervised texture segmentation using markov random field models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 478–482 (1991)
Andrey, P., Tarroux, P.: Unsupervised segmentation of markov random field modeled textured images using selectionist relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 252–262 (1998)
Haindl, M.: Texture segmentation using recursive markov random field parameter estimation. In: Bjarne, K., Peter, J. (eds.) Proceedings of the 11th Scandinavian Conference on Image Analysis, Pattern Recognition Society of Denmark, Lyngby, Denmark, pp. 771–776 (1999)
Haindl, M., Havlíček, V.: Prototype implementation of the texture analysis objects.Technical Report 1939, ÚTIA AV ČR, Praha, Czech Republic (1997)
Carson, C., Thomas, M., Belongie, S., Hellerstein, J.M., Malik, J.: Blobworld: A system for region-based image indexing and retrieval. In: Third International Conference on Visual Information Systems, Springer, Heidelberg (1999)
Vision texture (vistex) database. Technical report, Vision and Modeling Group, http://www-white.media.mit.edu/vismod/
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)
Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K., Eggert, D.W., Fitzgibbon, A., Fisher, R.B.: An experimental comparison of range image segmentation algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 673–689 (1996)
Cheng, H., Jiang, X., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)
Fu, K., Mui, J.: A survey on image segmentation. Pattern Recognition 13, 3–16 (1981)
Gimel’farb, G.L.: Image Textures and Gibbs Random Fields., vol. 16. Kluwer Academic Publishers, Dordrecht (1999)
Kato, Z., Pong, T.C., Qiang, S.: Multicue MRF image segmentation: Combining texture and color features. In: Proc. International Conference on Pattern Recognition, IEEE, Los Alamitos (2002)
Khotanzad, A., Chen, J.Y.: Unsupervised segmentation of textured images by edge detection in multidimensional features. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI 11, 414–421 (1989)
Meil, M., Heckerman, D.: An experimental comparison of model-based clustering methods. Mach. Learn. 42, 9–29 (2001)
Pal, N.R., Pal, S.: A review on image segmentation techniques. Pattern Recognition 26, 1277–1294 (1993)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)
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Haindl, M., Mikeš, S. (2004). Model-Based Texture Segmentation. 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_38
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DOI: https://doi.org/10.1007/978-3-540-30126-4_38
Publisher Name: Springer, Berlin, Heidelberg
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