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Title:
a pixel-based color image segmentation using support vector machine and fuzzy c-means
Author: Wang Xiang-Yang ; Zhang Xian-Jin ; Yang Hong-Ying ; Bu Juan
Source: Neural Networks
Issued Date: 2012
Volume: 33, Pages:148-159
Indexed Type: ei
Department: (1) School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China; (2) State Key Laboratory of Information Security, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
English 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. © 2012 Elsevier Ltd.
Language: 英语
WOS ID: WOS:000307430900014
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14727
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Wang Xiang-Yang,Zhang Xian-Jin,Yang Hong-Ying,et al. a pixel-based color image segmentation using support vector machine and fuzzy c-means[J]. Neural Networks,2012-01-01,33:148-159.
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