Comparing Texture Analysis Methods through Classification

The development and testing of two techniques of texture analysis based on different mathematical tools—the semivariogram and the Fourier spectra—are presented. These are also compared against a benchmark approach: the Gray-Level Co-occurrence Matrix. The three methods and their implementation are briefly described. Three series of experiments have been prepared to test the performance of these methods in various classification contexts. These contexts are simulated by varying the number, type and visual likeness of the texture patches used in classification tests. More specifically, their ability to correctly classify, separate, and associate texture patches is assessed. Results suggest that the classification context has an important impact on performance rates of all methods. The variogram-based and the Gray-Tone Dependency Matrix methods were generally superior, each one in particular contexts.

[1]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[2]  B. Julesz TEXTURE AND VISUAL PERCEPTION. , 1965, Scientific American.

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  L. O. Harvey,et al.  Visual texture perception and Fourier analysis , 1978, Perception & psychophysics.

[5]  Olivier D. Faugeras,et al.  Visual Discrimination of Stochastic Texture Fields , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[8]  N. Cressie,et al.  Robust estimation of the variogram: I , 1980 .

[9]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  B. Julesz Textons, the elements of texture perception, and their interactions , 1981, Nature.

[11]  T Caelli,et al.  On discriminating visual textures and images , 1982, Perception & psychophysics.

[12]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[13]  J. Kittler Image processing for remote sensing , 1983, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[14]  Mike James,et al.  Classification Algorithms , 1986, Encyclopedia of Machine Learning and Data Mining.

[15]  W. Stromberg,et al.  A Fourier-Based Textural Feature Extraction Procedure , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Alan H. Strahler,et al.  On the nature of models in remote sensing , 1986 .

[17]  C. Woodcock,et al.  The use of variograms in remote sensing: I , 1988 .

[18]  D. Peddle,et al.  Spectral texture for improved class discrimination in complex terrain , 1989 .

[19]  C. Woodcock,et al.  Autocorrelation and regularization in digital images. II. Simple image models , 1989 .

[20]  G. Ramstein,et al.  Analysis of the structure of radiometric remotely-sensed images , 1989 .

[21]  Theodosios Pavlidis,et al.  Integrating Region Growing and Edge Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Harry Wechsler,et al.  Segmentation of Textured Images and Gestalt Organization Using Spatial/Spatial-Frequency Representations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[24]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[25]  Giles M. Foody,et al.  On the compensation for chance agreement in image classification accuracy assessment, Photogram , 1992 .

[26]  Haim J. Wolfson,et al.  Texture classification in aerial photographs and satellite data , 1992 .

[27]  James R. Carr,et al.  Application of the semivariogram textural classifier (STC) for vegetation discrimination using SIR-B data of Borneo , 1992 .

[28]  Yung-Chang Chen,et al.  Statistical feature matrix for texture analysis , 1992, CVGIP Graph. Model. Image Process..

[29]  S. R. Rotman,et al.  Texture classification using the cortex transform , 1992, CVGIP Graph. Model. Image Process..

[30]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[31]  William E. Higgins,et al.  Texture Segmentation using 2-D Gabor Elementary Functions , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Graham Jones,et al.  Image segmentation using texture boundary detection , 1994, Pattern Recognit. Lett..

[33]  B. St-Onge,et al.  Estimating forest stand structure from high resolution imagery using the directional variogram , 1995 .

[34]  APPROCHE MULTIPOLARISATION ET TEXTURALE POUR LA RECONNAISSANCE DES CULTURES À L'AIDE DE DONNÉES RADAR AÉROPORTÉ , 1995 .

[35]  P. Atkinson Regularizing variograms of airborne MSS imagery , 1995 .

[36]  R. Lark Geostatistical description of texture on an aerial photograph for discriminating classes of land cover , 1996 .

[37]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Onkar Dikshit,et al.  Textural classification for ecological research using ATM images , 1996 .

[39]  Geoffrey J. Hay,et al.  An object-specific image-texture analysis of H-resolution forest imagery☆ , 1996 .

[40]  Yun Zhang,et al.  Texture-Integrated Classification of Urban Treed Areas in High-Resolution Color-Infrared Imagery , 2001 .

[41]  S. Franklin Using spatial Co-occurrence texture to increase forest structure and species composition classification accuracy , 2001 .

[42]  T. Warner,et al.  SCALE AND TEXTURE IN DIGITAL IMAGE CLASSIFICATION , 2002 .

[43]  D. Sagi,et al.  Gabor filters as texture discriminator , 1989, Biological Cybernetics.

[44]  Jiang Zetao,et al.  Unsupervised Texture Segmentation Based on FCM , 2005 .