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Title:
ls-svm based image segmentation using color and texture information
Author: Yang Hong-Ying ; Wang Xiang-Yang ; Wang Qin-Yan ; Zhang Xian-Jin
Source: Journal of Visual Communication and Image Representation
Issued Date: 2012
Volume: 23, Issue:7, Pages:1095-1112
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 partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we present an effective color image segmentation approach based on pixel classification with least squares support vector machine (LS-SVM). Firstly, the pixel-level color feature, Homogeneity, is extracted in consideration of local human visual sensitivity for color pattern variation in HSV color space. Secondly, the image pixel's texture features, Maximum local energy, Maximum gradient, and Maximum second moment matrix, are represented via Gabor filter. Then, both the pixel-level color feature and texture feature are used as input of LS-SVM model (classifier), and the LS-SVM model (classifier) is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained LS-SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of LS-SVM classifier. Experimental evidence shows that the proposed method has very effective segmentation results and computational behavior, 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 Inc. All rights reserved.
Language: 英语
WOS ID: WOS:000309247900013
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14719
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Yang Hong-Ying,Wang Xiang-Yang,Wang Qin-Yan,et al. ls-svm based image segmentation using color and texture information[J]. Journal of Visual Communication and Image Representation,2012-01-01,23(7):1095-1112.
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