A framework for texture classification using the coordinated clusters representation
A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. The ability of the descriptor to capture spatial statistical features of an image is exploited. A binarization needed for image preprocessing is done using, but not restricted to, a fuzzy clustering algorithm. A normalized spectrum histogram of the coordinated cluster representation is used as a unique feature vector, and a simple minimum distance classifier is used for classification purposes. Using the size and the number of subimages for prototype generation and the size of the test images as the parameters in the learning and recognition phases, we establish the regions of reliable classification in the space of subimage parameters. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification. (C) 2002 Elsevier Science B.V. All rights reserved.
texture classification coordinated clusters representation binarization fuzzy clustering