Rotation-invariant method for texture matching using model-based histograms and GLCM

Abstract

Nowadays, researchers are interested in informatics systems that process automatically the information. Image is an interesting research area due to the growth of the technologies. In this paper, we have proposed a new texture analysis method. One of the key problems in image processing is the rotation. Therefore, the proposed method is robust against rotation. The goal of this study is to construct a model from each texture. After that, the system classifies the query texture based on the extracted texture models. In this work, we applied a recent and efficient feature extraction method called rotation invariant neighbourhood-based binary pattern (RINBP). The proposed system combines between two parts. First, extract the RINBP model from the texture. Second, we apply the GLCM method in order to extract statistical measures. In the experiments, we have used the Brodats album database. Experimental parts illustrate the efficiency and the robustness of the proposed system against rotation.