Color image segmentation using histogram thresholding – Fuzzy C-means hybrid approach

• Published in: Pattern recognition Vol. 44; no. 1; pp. 1 - 15
• Main Authors: Siang Tan, Khang, Mat Isa, Nor Ashidi
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 2011; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Color image segmentation; Computer science; control theory; systems; Exact sciences and technology; Fuzzy C-means; Histogram thresholding; Image processing; Information, signal and communications theory; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Signal processing; Telecommunications and information theory; Histogram thresholding; Fuzzy C-means; Color image segmentation; Image segmentation; Computer vision; Histogram; Image processing; Threshold detection; Hybrid model; Pattern recognition; Algorithm; Color image
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2010.07.013

This paper presents a novel histogram thresholding – fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is utilized to improve the compactness of the clusters forming these uniform regions. Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm.