@Article{JICS-10-063, author = {Rasigiri Venkata Lakshmi , E. Srinivasa Reddy and K. Chandra Sekharaiah}, title = {Texture Classification based on Fuzzy Based Texton Cooccurrence Matrix}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {10}, number = {1}, pages = {063--074}, abstract = { The Applications of Pattern recognition like wood classification, stone and rock classification problems, the major usage techniques ate different texture classification techniques. Generally most of the problems used statistical approach for texture analysis and texture classification. Gray Level Co-occurrence Matrices (GLCM) approach is particularly applied in texture analysis and texture classification. The GLCM gives better results with accuracy but its take much time for computation. The texture analysis methods mainly depends upon how the particular texture features characterizes texture image. The accuracy of a particular texture analysis method depends what type of features are extracted from a texture image for classification, whether these features correctly classifies the textures or not. The accuracy of texture analysis method depends not only the texture features are important but also the way in which texture features are applied is also an important and significant for a critical, particular and perfect texture classification and analysis. The present paper derived a new texture analysis method i.e. co-occurrence matrix based on fuzzy rules and water shed texton patterns. The present paper applies fuzzy rules on Original texture image based on water shed texton patterns and generates Co-occurrence matrices derived a new matrix called Fuzzy based Texton Co-occurrence Matrices (FbTCoM) for texture classification. The present paper integrates the advantages of co-occurrence matrix and texton image by representing the attribute of co-occurrence matrix using water shed texton pattern based on fuzzy rule. The co-occurrence features extracted from the FbTCoM provides complete texture information about a Texture image. The proposed method is experimented on Vistex, Brodatz textures, CUReT, MAYAGAN, PBOURKE, and Google color texture images. The experimental results indicate the proposed method classification performance is superior to that of many existing methods. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22566.html} }