A Combinatorial K-View Based Algorithm for Image Texture Classification

Textural features is very important properties in many types of images. Partitioning an image into homogeneous regions based on textural features is useful in computer vision. Many texture classification algorithms have been proposed including Local Binary Patterns, Gray Level Co-Occurrence and K-View based algorithms, to name a few. Among of them, The K-View using Rotation-invariant feature algorithm (K-View-R) and the fast weighted K-View-Voting algorithm (K-View-V) produce higher classification accuracy by compare with those original K-View based algorithms. However, there still have some rooms for improvement. In this paper, by analyzing those K-View based algorithms, an attempt to utilize the advantages of the K-View-R and K-View-V was investigated. The new approach which we called combinatorial K-View based method was presented. To test and evaluate the proposed method, some experiments were carried out on a lot of textural images which taken from a standard database. Preliminary experimental results demonstrated the new method achieved more accurate classification by compare with other K-View based methods.