a comparative study and analysis on k-view based algorithms for image texture classification
Several K-View based algorithms have been made for developing image texture classification. These are K-View-Template algorithm (K-View-T), K-View-Datagram algorithm (K-View-D), Fast Weighted K-View-Voting algorithm (K-View-V), and K-View Using Rotation-Invariant Feature algorithm (K-View-R). In this paper, we review those K-View based algorithms and perform an empirical study for comparison by using classification accuracy, efficiency and stability. In addition, we propose a new K-View algorithm Using Gray Level Co-Occurrence Matrix algorithm (K-View-G) which also is compared with other K-View algorithms to demonstrate its effectiveness in image texture classification.
image texture classification k-view algorithms gray level co-occurrence matrix (glcm)