Texture classification based on fuzzy similarity texton co-occurrence matrix

Abstract

In the existing texton based methods a texton is derived in a grid by a collection of pixels exhibiting exactly the similar grey level values/color/attributes. The disadvantage of this approach is they fail in recognizing textons, whenever a small random noise changes the pixels intensity values slightly. This paper addresses this by deriving a fuzzy similarity ‘S’ in identification of texton patterns. The proposed Fuzzy similarity Texton Co-occurrence Matrix (FSTCM) framework considers the pixels whose gray level value falls within the fuzzy similarity index value as texton pattern. The FSTCM divides initially the texture image into micro regions of size 2x2, identifies the textons and transforms the texture image into a fuzzy texton image. This paper derives gray level co-occurrence matrix (GLCM) features on FSTCM and the proposed method is tested on five popular texture image databases. The experimental investigation reveals the high performance of the proposed method over the state of art local based and texton based methods.