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 CoOccurrence and K-View based algorithms, to name a few. Among of them, The K-View using Rotationinvariant 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.
Texture classification Voting K-View algorithms
Yihua Lan Haozheng Ren Yi Chen
School of Computer Engineering Huaihai Institute of Technology Lianyungang, China School of Computer Science Hubei University of Technology Wuhan, China
国际会议
南昌
英文
531-534
2012-08-26（万方平台首次上网日期，不代表论文的发表时间）