Pattern Recognition of Fabric Defects Using Case-Based Reasoning

In this paper, we evaluate the efficiency and accuracy of a method of detecting fabric defects that have been classified into different categories by case-based reasoning (CBR). It shows significant promise for improving the effectiveness of complex and unstructured decision making. Four kinds of fabric defects most likely to be found during weaving were learned by CBR, which is both a paradigm for computer-based problem-solvers and a model of human cognition. The method used for processing image feature extraction is a co-occurrence-based method, by means of which six feature parameters are obtained. However, the design of appropriate case-retrieval mechanisms is still challenging. This paper presents a genetic algorithm (GA)-based approach to enhance the case-matching process. The results show that fabric defects inspected by means of image recognition in accordance with the CBR agree approximately with initial expectation.