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

Authors

  • Murugeswari Palanivel Sri Vidya College of Engineering and Technology
  • Manimegalai Duraisamy National Engineering College

DOI:

https://doi.org/10.4186/ej.2012.16.5.115

Keywords:

Image segmentation.

Abstract

Segmentation is an essential process in image because of its wild application such as image analysis, medical image analysis, pattern reorganization, etc. 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 (ICICM). 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 state-of-art image segmentation algorithms.

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Author Biographies

Murugeswari Palanivel

Department of IT, Sri Vidya College of Engineering and Technology, Sivakasi Main Road, Virudhunagar, Tamil Nadu, India

Manimegalai Duraisamy

Department of IT, National Engineering College, K. R. Nagar, Kovilpatti, Kovilpatti, Tamil Nadu, India

Published

Vol 16 No 5, Jul 8, 2012

How to Cite

[1]
M. Palanivel and M. Duraisamy, “Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach”, Eng. J., vol. 16, no. 5, pp. 115-126, Jul. 2012.