A Model-Based Method for Rotation Invariant Texture Classification

This paper presents a new model-based approach for texture classification which is rotation invariant, i.e., the recognition accuracy is not affected if the orientation of the test texture is different from the orientation of the training samples. The method uses three statistical features, two of which are obtained from a new parametric model of the image called a ``circular symmetric autoregressive model.'' Two of the proposed features have physical interpretation in terms of the roughness and directionality of the texture. The results of several classification experiments on differently oriented samples of natural textures including both microtextures and macrotextures are presented.

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

[2]  Ruzena Bajcsy,et al.  Computer Description of Textured Surfaces , 1973, IJCAI.

[3]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[4]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  S. Zucker.  Toward a model of texture , 1976 .

[6]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

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

[8]  Azriel Rosenfeld,et al.  Mosaic Models for Textures , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  King-Sun Fu,et al.  Stochastic tree grammar inference for texture synthesis and discrimination , 1979 .

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

[11]  Olivier D. Faugeras,et al.  Decorrelation Methods of Texture Feature Extraction , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  L. S. Davis.  Image Texture Analysis Techniques - a Survey , 1980 .

[13]  M. Hassner,et al.  The use of Markov Random Fields as models of texture , 1980 .

[14]  James W. Modestino,et al.  Texture Discrimination Based Upon an Assumed Stochastic Texture Model , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  R. L. Kashyap,et al.  Analysis and Synthesis of Image Patterns by Spatial Interaction Models , 1981 .

[16]  Larry S. Davis,et al.  Polarograms: A new tool for image texture analysis , 1979, Pattern Recognit..

[17]  C. H. Chen,et al.  A Study of Texture Classification Using Spectral Features , 1982 .

[18]  Peter de Souza,et al.  Texture recognition via autoregression , 1982, Pattern Recognit..

[19]  Rama Chellappa,et al.  Texture classification using features derived from random field models , 1982, Pattern Recognit. Lett..

[20]  James W. Modestino,et al.  A Maximum Likelihood Approach to Texture Classification , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Rama Chellappa,et al.  Estimation and choice of neighbors in spatial-interaction models of images , 1983, IEEE Trans. Inf. Theory.

[23]  R. Nevatia.  Structural Analysis ofNatural Textures , 1986 .

[24]  Ramakant Nevatia,et al.  Structural Analysis of Natural Textures , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .