A novel image recognition using Fuzzy C-Means and content-based fabric image retrieval

Abstract

ABSTRACT Due to the growth of image exploitation, there is a requirement of systemizing the images in several ways. Time consumption and accuracy are the most important problem in the CBIR method. Therefore, a Content-Based Fabric Image Retrieval (CBFIR) method is proposed in this paper that is based on texture and colour features extraction. The Fuzzy C-Means (FCM) algorithm creates primary clusters and the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm updates every cluster in the dictionary. To find the sparse representation dictionary, the test image is compared by the proposed method with the dictionaries generated. The input images are obtained from the fabric dataset. Several distance based similarity measures are utilized for comparison. It can be seen that the proposed method’s performance is sufficiently good concerning the rate of recall, accuracy, and precision with the values of 97%, 91%, and 88% respectively.