Image classification using concatenated of co-occurrence matrix features and local ternary patterns
Texture, color, and shape are the three main components that the human visual brain uses to identify or identify environments and objects. Therefore, tissue classification has been considered by many scientific researchers in the last decade. The texture features can be used in many different vision and machine learning problems. As of now, various methods have been proposed for classifying tissues. In all methods, the accuracy of the classification is a major challenge that needs to be improved. This article presents a new method based on a combination of two efficient tissue descriptors, the co-occurrence matrix and local ternary patterns (LTP). First, the local binary pattern and LTP are performed to extract information from the local tissue. In the next step, a subset of statistical properties is extracted from the gray surface concurrency matrices. Finally, the interconnected features are used to teach classification. Performance is evaluated for accuracy on the Brodatz reference data set. The experimental results show that the proposed method offers a higher degree of classification compared to some advanced methods.