Color Textured Image Segmentation Using ICICM Interval Type-2 Fuzzy C-means Clustering Hybrid Approach

Segmentation is an essential process in image processing because of its wild application such as image analysis, medical image analysis and pattern recognition. Color and texture are most significant low-level features in an image. Normally, color textured image segmentation consists of two steps: (i) extracting the feature and (ii) clustering the feature vector. This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix. Then, Extended Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image. Experimental results show that the proposed hybrid approach could obtain better cluster quality and segmentation results compared to stateof-art image segmentation algorithms.