Abstract
The task of texture segmentation is to identify image curves that separate different textures. To segment textured images, one must first be able to discriminate textures. A segmentation algorithm performs texture-discrimination tests at densely spaced image positions, then interprets the results to localize edges. This article focuses on the first stage, texture discrimination.
We distinguish between perceptual and physical texture differences: the former differences are those perceived by humans, while the latter, on which we concentrate, are those defined by differences in the processes that create the texture in the scene. Physical texture discrimination requires computing image texture measures that allow the inference of physical differences in texture processes, which in turn requires modeling texture in the scene. We use a simple texture model that describes textures by distributions of shape, position, and color of substructures. From this model, a set of image texture measures is derived that allows reliable texture discrimination. These measures are distributions of overall substructure length, width, and orientation; edge length and orientation; and differences in averaged color. Distributions are estimated without explicitly isolating image substructures. Tests of statistical significance are used to compare texture measures.
A forced-choice method for evaluating texture measures is described. The proposed measures provide empirical discrimination accuracy of 84 to 100% on a large set of natural textures. By comparison, Laws' texture measures provide less than 50% accuracy when used with the same texture-edge detector. Finally, the measures can distinguish textures differing in second-order statistics, although those statistics are not explicitly measured.
Similar content being viewed by others
References
N. Ahuja and B. Schachter, Pattern Models, Wiley: New York, 1983.
J. Aloimonos and M. Swain, “Shape from patterns: Regularization,” Intern. J. Comput. Vision 2(2):171–187, 1988.
T.W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd ed. Wiley: New York, 1984.
J. Beck, K. Prazdny, and A. Rosenfeld, “A theory of textural segmentation.” In J. Beck, B. Hope, and A. Rosenfeld (eds.), Human and Machine Vision. Academic Press: New York, pp. 1–38, 1982.
T.O. Binford, “Figure/ground: Segmentaton and aggregation.” In O.J. Braddick and A.C. Sleigh (eds.), Physical and Biological Processing of Images. Springer-Verlag: New York, 1983.
D. Blostein and N. Ahuja, “Representation and three-dimensional interpretation of image texture: an integrated approach,” Proc. Intern. Conf. Comput. Vision, pp. 444–449, London, 1987.
P. Brodatz, Textures: A photographic album for artists and designers. Dover: New York, 1966.
A. Bruno, “Structural analysis of textures: a solution for describing primitives,” Proc. Intern. Conf. Pattern Recog., pp. 817–820, Paris, 1986.
T. Caelli, “Three processing characteristics of visual texture segmentation,” Spatial Vision 1(1):19–30, 1985.
R.W. Conners and C.A. Harlow, “A theoretical comparison of texture algorithms,” IEEE Trans. PAMI 2(3):204–222, 1980.
R.W. Conners and C.A. Harlow, “Toward a structural textural analyzer based on statistical methods.” In A. Rosenfeld (ed.), Image Modeling, Academic Press: New York, pp. 29–61, 1981.
T.N. Cornsweet, Visual Perception. Academic Press: New York, 1970.
L.S. Davis, “Computing the spatial structure of cellular textures,” Comput. Graphics Image Process. 11:111–122, 1979.
L.S. Davis, M. Clearman, and J.K. Aggarwal, “An empirical evaluation of generalized co-occurrence matrices,” IEEE Trans. PAMI 3(2):214–221, 1981.
L.S. Davis, S. Johns, and J.K. Aggarwal, “Texture analysis using generalized co-occurrence matrices,” IEEE Trans. PAMI 1: 251–258, 1979.
L.S. Davis and A. Mitiche, “Edge detection in textures,” Comput. Graphics Image Process. 12(1):25–39, 1980.
L.S. Davis and A. Mitiche, “MITES: A model-driven, iterative texture segmentaton algorithm,” Comput. Graphics Image Process. 19:95–110, 1982.
A. Gagalowicz and S.D. Ma, “Sequential synthesis of natural textures,” Comput. Vision, Graphics, Image Process. 30:289–315, 1985.
R.M. Haralick, “Digital step edges from zero crossing of second directional derivatives,” IEEE Trans. PAMI 6(1):58–68, 1984.
R.M. Haralick, “Edge and region analysis for digital image data,” Comput. Graphics Image Process. 12:60–73, 1980.
R.M. Haralick, “Statistical and structural approaches to texture,” Proc. IEEE 67(5):786–804, 1979.
R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst., Man and Cybern. 3(6):610–621, 1973.
D. Harwood, M. Subbarao, and L.S. Davis, “Texture classification by local rank correlation,” Comput. Vision, Graphics, Image Process. 32:404–411, 1985.
G. Healey and T.O. Binford, “A color metric for computer vision,” Proc. IEEE Conf. Comput. Vision Pattern Recog., Ann Arbor, 1988.
G. Healey and T.O. Binford, “The role and use of color in a general vision system,” Proc. DARPA Image Understanding Workshop, pp. 599–613, Los Angeles, 1987.
B. Julesz, “The role of terminators in preattentive perception of line textures.” In S. Levin (ed.) Recognition of Pattern and Form, Springer-Verlag: Berlin, pp. 33–58, 1982.
B. Julesz, “Texton gradients: the texton theory revisited,” Biological Cybernetics 54:245–251, 1986.
B. Julesz, “Textons, the elements of textural perception, and their interactions,” Nature 290:91–97, 1981.
B. Julesz and J.R. Bergen, “Textons, the fundamental elements in preattentive vision and perception of textures,” Bell System Tech. J. 62(6):1619–1645, 1983.
K.I. Kanatani and T.-C. Chou, “Shape from texture: general principle,” Artificial Intelligence 38(1):1–48, 1989.
R.L. Kashyap, “Image Models.” In T.Y. Young and K.-S. Fu (eds.) Handbook of Pattern Recognition and Image Processing, Academic Press: Orlando FL, pp. 281–310, 1986.
R.L. Kashyap and A. Khotanzad, “A model-based method for rotation invariant texture classification,” IEEE Trans. PAMI 8(4): 472–481, 1986.
B. Kjell and C.R. Dyer, “Edge separation and orientation texture measures,” Tech. Rep. 559, Computer Science Dept., Univ. of Wisconsin, Madison, October 1984.
K.I. Laws, “Goal-directed textured-image segmentation,” Tech. Rep. 334, AI Center, SRI International, September 1984.
K.I. Laws, “Rapid texture identification.” In Image Processing for Missile Guidance, Proc. SPIE vol 238, pp. 376–380, 1980.
K.I. Laws, “Textured image segmentation,” Tech. Rep. UCSIPI 940, USC Image Proc. Institute, Univ. of Southern Calif., 1980.
Y. Leclerc, “Capturing the local structure of image discontinuities in two dimensions,” Proc. IEEE Conf. Comput. Vision Pattern Recog., pp. 34–38, San Francisco, 1985.
J.-G. Leu and W. Wee, “Detecting the spatial structure of natural textures based on shape analysis,” Comput. Vision, Graphics, Image Process. 31:67–88, 1985.
C.-E. Liedtke, “Image segmentation considering properties of the human visual system.” In T.S. Huang (ed.) Image Sequence Processing and Dynamic Scene Analysis Springer-Verlag. New York, 1983.
J.T. Maleson, C.M. Brown, and J.A. Feldman, “Understanding natural texture,” Proc. DARPA Image Understanding Workshop, pp. 19–27, October 1977.
D. Marr, “Analyzing natural images: a computational theory of texture vision.” Cold Spring Harbor Symposium on Quantatitive Biology, 40:647–662, 1975.
T. Matsuyama, K. Saburi, and M. Nagao, “A structural analyzer for regularly arranged textures,” Comput. Graphics Image Process. 18:259–278, 1982.
J.L. Muerle, “Some thoughts on texture discrimination by computer.” In B.S. Lipkin and A. Rosenfeld (eds.), Picture Processing and Psychopictorics. Academic Press: New York, pp. 371–379, 1970.
S. Peleg, J. Naor, R. Hartley, and D. Avnir, “Multiple resolution texture analysis and classification,” IEEE Trans. PAMI 6(4):518–523, 1984.
A. Pentland, “Fractal-based description of natural scenes,” IEEE Trans. PAMI 6(6):661–674, 1984.
M. Pietikaïnen, A. Rosenfeld, and L.S. Davis, “Experiments with texture classification using averages of local pattern matches,” IEEE Trans. Syst., Man and Cybern. 13(3):421–426, 1983.
H.M. Raafat and A.K.C. Wong, “Texture-based image segmentation,” Proc. IEEE Conf. Comput. Vision Pattern Recog., pp. 469–475, Miami Beach, 1986.
T.C. Rearick, “A texture analysis algorithm inspired by a theory of preattentive vision,” Proc. IEEE Conf. Comput. Vision Pattern Recog., pp. 312–317, San Francisco, 1985.
L. Sachs, Applied Statistics. Springer-Verlag: New York, 1984.
H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Trans. Syst., Man and Cybern. 8(6):460–473, 1978.
F. Tomita, Y. Shirai, and S. Tsuji, “Description of textures by a structural analysis,” Proc. 6th Intern. Joint Conf. Artif. Intell., pp. 884–889, Tokyo, 1979.
A. Treisman, “A feature-integration theory of attention,” Cognitive Psychology 12:97–136, 1980.
A. Treisman, “Preattentive processing in vision,” Comput. Vision, Graphics, Image Process. 31(2):156–177, 1985.
S. Tsuji and F. Tomita, “A structural analyzer for a class of textures,” Comput. Graphics Image Process. 2:216–231, 1973.
L. van Gool, P. Dewaele, and A. Oosterlinck, “Texture analysis anno 1983,” Comput. Vision, Graphics, Image Process. 29:336–357, 1985.
F. Vilnrotter, R. Nevatia, and K. Price, “Structural analysis of natural textures,” IEEE Trans. PAMI 8:76–89, 1986.
R. Vistnes, “Computer Texture Analysis and Segmentation,” Ph.D. thesis, Computer Science Dept., Stanford University, June 1988.
H. Voorhees and T. Poggio, “Detecting textons and texture boundaries in natural images,” Proc. Intern. Conf. Comput. Vision, pp. 250–258, London, 1987.
D. Wermser and C.-E. Liedtke, “Texture analysis using a model of the visual system,” Proc. 6th Intern. Conf. Pattern Recog., pp. 1078–1080, Munich, 1982.
J.S. Weszka, C.R. Dyer, and A. Rosenfeld, “A comparative study of texture features for terrain classification,” IEEE Trans. Syst., Man and Cybern. 6:269–285, 1976.
A.P. Witkin, “Recovering surface shape and orientation from texture,” Artificial Intelligence 17:17–45, 1981.
S.W. Zucker, “On the structure of texture,” Perception 5:437–459, 1976.
Author information
Authors and Affiliations
Additional information
The author was with the Robotics Laboratory, Computer Science Department, Stanford University, Stanford, California 94305. He is now with the Institut National de Recherche en Informatique et en Automatique (INRIA), Sophia-Antipolis, 2004 Route des Lucioles, 06565 Valbonne Cedex, France.
Rights and permissions
About this article
Cite this article
Vistnes, R. Texture models and image measures for texture discrimination. Int J Comput Vision 3, 313–336 (1989). https://doi.org/10.1007/BF00132602
Issue Date:
DOI: https://doi.org/10.1007/BF00132602