Belief Rule Base Inference for Texture Image Classifcation
Received date: 2016-10-17
Revised date: 2017-04-25
Online published: 2017-09-30
To improve precision of traditional texture image classify algorithm, a new texture image classifcation method based on belief rule-base inference methodology using evidential reasoning approach(RIMER) is proposed. Researches on texture image classifcation generally consider improving texture feature extraction, and the design of classifer that is crucial to classifcation precision is largely ignored. In this paper, a rule-base inference method using an evidential reasoning approach is proposed. The classifer is redesigned based on the current methods of texture feature extraction. Algorithms of angular-radialtransform and gray-level con-occurrence matrix are used to extract texture image feature. Principle component analysis is carried out to solve the problem that the size of a belief rule base(BRB) classifer is controlled within a feasible range. The approach of rule-base inference method with evidential reasoning transforms the texture features into classifed belief degree information. Practicability and effectiveness of the proposed approach is validated in a case study.
FANG Zhi-jian, FU Yang-geng, CHEN Jian-hua . Belief Rule Base Inference for Texture Image Classifcation[J]. Journal of Applied Sciences, 2017 , 35(5) : 545 -558 . DOI: 10.3969/j.issn.0255-8297.2017.05.002
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