Abstract
Many applications such as image compression, pre-processing or segmentation require some information from the regions composing an image. The main objective of this paper is to define a methodology to extract some local information from an image. Each region is characterized in terms of homogeneity (region composed with the same grey-level or a single texture) and its type (textured or uniform). The decision criterion is based on the use of classical texture attributes (cooccurrence matrix and grey-levels moments) and a support vector machine in order to realize the fusion of the different attributes. We then characterize each region considering its type by appropriate features.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Augusteijn, M.F., Clemens, L.E., Shaw, K.A.: Performance evaluation of textre measures for ground cover identification in satellite images by means of a neural network classifier. IEEE Transactions on Geoscience and Remote Sensing 33(3), 616–626 (1995)
Ballarin, V., Moler, E., Brun, M.: Scale invariant texture classification with mathematical morphology. In: EUSIPCO, vol. (4), pp. 2533–2536 (1998)
Brodatz, P.: Textures A photographic album for artists and designers. Dover, New York (1966)
Cartensen, J.M.: Cooccurence feature performance in texture classification. In: Proc. of the Scandinavian Conference on Image Analysis, vol. (1), pp. 831–838 (1993)
Chabrier, S., Rosenberger, C., Laurent, H., Emile, B., Marche, P.: Evaluating the segmentation result of a grey-level image. In: EUSIPCO (2004)
Hofmann, T., Puzicha, J., Buhmann, J.M.: Unsupervised Texture Segmentation in a Deterministic Annealing Framework. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 803–818 (1998)
Haralick, R.M., Shanmungan, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems Man and Cybernetics 6(3) (1979)
Manian, V., Vasquez, R., Katiyar, P.: Texture classification using logical operators. IEEE Transactions on image processing 9(10), 1693–1703 (2000)
Puzicha, J., Hofmann, T., Buhmann, J.M.: Histogram Clustering for Unsupervised Segmentation and Image retrieval. Pattern Recognition Letters 20(9), 899–909 (1999)
Rosenfeld, A., Troy, E.: Visual Texture Analysis, Tech. Report, 70-116 (1970)
Rosenberger, C.: Implementation of an Adaptive Image Segmentation System, Phd thesis Université de Rennes I (1999)
Rosenberger, C., Chehdi, K.: Towards a complete adaptive analysis of an image. International Journal of Electronic Imaging 12(2), 292–298 (2003)
Singh, M., Singh, S.: Texture algoithms: performance variability across data sets. Taylor and Francis, Cybernetics and Systems 34(1) (2003)
Valkealahti, K., Oja, E.: Reduced Multidimensional Cooccurrence Histograms in Texture Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1) (1998)
Won, C.S., Choe, Y.: Image block classification using stochastic image segmentation. Electronics Letters 32(16), 1462–1463 (1996)
Zhu, S.C., Liu, X.W., Wu, Y.N.: Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo. Toward a trichromacy theory of texture. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(6), 554–569 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chabrier, S., Emile, B., Rosenberger, C. (2005). Texture Detection for Image Analysis. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_51
Download citation
DOI: https://doi.org/10.1007/11552499_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
eBook Packages: Computer ScienceComputer Science (R0)