Analysis and synthesis of textures: a co-occurrence-based approach

Abstract The selection of suitable features is the most critical part of any classification process. In the area of textural classification, one way to determine if the features are suitable is to synthesize an image which has the given textural features. An algorithm for synthesizing textures that have a set of given cooccurrence matrices is presented, and it is shown that these synthetic images do indeed match their real counterparts very closely. The successful synthesis of textures motivates the use of co-occurrences as features for texture classification. A new algorithm for classifying textures based on co-occurrence feature vectors that are modelled as multinomial density functions is presented.

[1]  A. Khotanzad,et al.  Feature selection for texture recognition based on image synthesis , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  James M. Keller,et al.  Texture description and segmentation through fractal geometry , 1989, Comput. Vis. Graph. Image Process..

[3]  R. Gallager.  Information Theory and Reliable Communication , 1968 .

[4]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[5]  Songde Ma,et al.  Sequential synthesis of natural textures , 1985, Comput. Vis. Graph. Image Process..

[6]  Gabriele Lohmann.  Co-occurrence-based analysis and synthesis of textures , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[7]  Kenneth I. Laws,et al.  Goal-Directed Textured-Image Segmentation , 1985, Other Conferences.

[8]  André Gagalowicz,et al.  A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Gabriele Lohmann.  Assessment of textural features for remote sensing applications , 1994, Other Conferences.

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

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

[13]  Kenneth I. Laws,et al.  Rapid Texture Identification , 1980, Optics & Photonics.

[14]  Gabriele Lohmann.  An Evidential Reasoning Approach to the Classification of Satellite Images , 1991, ECSQARU.

[15]  Songde Ma,et al.  Model driven synthesis of natural textures for 3-D scenes , 1986, Comput. Graph..

[16]  Rama Chellappa,et al.  Texture synthesis and compression using Gaussian-Markov random field models , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Richard C. Dubes,et al.  Performance evaluation for four classes of textural features , 1992, Pattern Recognit..