A pixel-based color image segmentation using support vector machine and fuzzy C-means

Authors:
Xiang-Yang Wang;Xian-Jin Zhang;Hong-Ying Yang;Juan Bu
Affiliations:
School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China and State Key Laboratory of Information Security, Institute of Software, Chinese Academy of Sciences ...;School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China;School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China;School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China
Venue:
Neural Networks
Year:
2012

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Abstract

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.