Open AccessAn Advanced Approach to Extraction of Colour Texture Features Based on GLCM
Author(s) -
Miroslav Benčo,
Róbert Hudec,
Patrik Kamencay,
Martina Zachariášová,
Slavomír Matúška
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/58692
Subject(s) - artificial intelligence , computer science , grey level , texture (cosmology) , pattern recognition (psychology) , feature extraction , computer vision , co occurrence matrix , computation , image texture , image (mathematics) , gabor filter , texture filtering , image processing , algorithm
This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show that it is a powerful method for colour texture classification