Abstract
Texture identification can be a key component in Content Based Image Recognition systems. Although formal definitions of texture vary in the literature, it is commonly accepted that textures are naturally extracted and recognized as such by the human visual system, and that this analysis is performed in the frequency domain. The method presented here employs a discrete Fourier transform in the polar space to extract features, which are then classified with a vector quantizer for supervised segmentation of images into texture regions. Experiments are conducted on a standard database of test problems that show this method compares favorably with the state-of-the-art and improves over previously proposed frequency-based methods.
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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 Co., Singapore (1998)
Clark, A.A., Thomas, B.T., Campbell, N.W., Greenway, P.: Texture Deconvolution for Fourier-Based Analysis of Non-Rectangular Regions. In: BMVC, pp. 193–202 (1999)
Pyun, K., Won, C.S., Lim, J., Gray, R.M.: Texture classification based on multiple Gauss mixture vector quantizer. In: Proc. of ICME, pp. 501–504 (2002)
Aiyer, A., Pyun, K., Huang, Y., O’Brien, D.B., Gray, R.M.: Lloyd Clustering of Gauss Mixture Models for Image Compression and Classification. Signal Processing: Image Communication (2005)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1996)
Randen, T., Husøy, J.H.: Filtering for Texture Classification: A Comparative Study. IEEE Transaction on Pattern Analysis and Machine Intelligence 21(4), 291–310 (1999)
Mäenpää, T., Pietikäinen, M., Ojala, T.: Texture Classification by Multi-Predicate Local Binary Pattern Operators. In: ICPR, pp. 3951–3954 (2000)
Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex – New Framework for Empirical Evaluation of Texture Analysis Algorithms. In: Proc. 16th Int. Conf. on Pattern Recognition, pp. 701–706 (2002)
Ojala, T., Pietikäinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29, 51–59 (1996)
Blakemore, C.B., Campbell, F.W.: On the existence of neurons in the human visual system selectivity sensitive to the orientation and size of retinal images. Journal of Physiology 203, 237–260 (1969)
Campbell, F.W., Cleland, B.G., Cooper, G.F., Enroth-Cugell, C.: The angular selectivity of visual cortical cells to moving gratings. J. Physiol. 198, 237–250 (1968)
Campbell, F.W., Nachmias, J., Jukes, J.: Spatial frequency discrimination in human vision. J. Opt. Soc. Am. 60, 555–559 (1970)
Maffei, L., Fiorentini, A.: The visual cortex as a spatial frequency analyzer. Vision Res. 13, 1255–1267 (1973)
Maffei, L., Fiorentini, A.: Spatial frequency rows in the striate visual cortex. Vision Res. 17, 257–264 (1977)
de Valois, R.L., Albrecht, D.G., Thorell, L.G.: Spatial frequency selectivity of cells in macaque visual cortex. Vision Res. 22, 545–559 (1982)
Kuwahara, M., Hachimura, K., Eiho, S., Kinoshita, M.: Digital Processing of Biomedical Images, pp. 187–203. Plenum Press, New York (1976)
MeasTxt (1998), http://www.cssip.elec.uq.edu.au/~guy/meastex/meastex.html
MIT Vision and Modeling Group (1998), http://www.media.mit.edu/vismod
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Di Lillo, A., Motta, G., Storer, J.A. (2007). Supervised Segmentation Based on Texture Signatures Extracted in the Frequency Domain. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_13
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DOI: https://doi.org/10.1007/978-3-540-72847-4_13
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