Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation

• Published in: IEEE transactions on image processing Vol. 22; no. 2; pp. 573 - 584
• Main Authors: Gong, Maoguo, Liang, Yan, Shi, Jiao, Ma, Wenping, Ma, Jingjing
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2013; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Clustering algorithms; Damping; Exact sciences and technology; Fuzzy clustering; gray-level constraint; Image processing; Image segmentation; Information, signal and communications theory; Kernel; kernel metric; Linear programming; Noise; Noise measurement; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; spatial constraint; Telecommunications and information theory; Automatic classification; Image processing; Expert system; Algorithm; Signal classification; Adaptive method; spatial constraint; Kernel method; Outlier; Image segmentation; gray-level constraint; Fuzzy clustering; Distance measurement; Metric; Robustness; Objective function; kernel metric
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2012.2219547

In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.