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Title:
LS-SVM-based image segmentation using pixel color-texture descriptors
Author: Yang, Hong-Ying ; Zhang, Xian-Jin ; Wang, Xiang-Yang
Keyword: Image segmentation ; Least squares support vector machine ; Human visual attention ; Local texture content ; Arimoto entropy thresholding
Source: PATTERN ANALYSIS AND APPLICATIONS
Issued Date: 2014
Volume: 17, Issue:2, Pages:341-359
Indexed Type: SCI
Department: [Yang, Hong-Ying; Zhang, Xian-Jin; Wang, Xiang-Yang] Liaoning Normal Univ, Sch Comp & Informat Technol, Dalian 116029, Peoples R China. [Wang, Xiang-Yang] Chinese Acad Sci, Inst Software, State Key Lab Informat Secur, Beijing 100190, Peoples R China.
Abstract: Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. Moreover, the increasing demands of image analysis tasks in terms of segmentation results' quality introduce the necessity of employing multiple cues for improving image-segmentation results. In this paper, we present a least squares support vector machine (LS-SVM) based image segmentation using pixel color-texture descriptors, in which multiple cues such as edge saliency, color saliency, local maximum energy, and multiresolution texture gradient are incorporated. Firstly, the pixel-level edge saliency and color saliency are extracted based on the spatial relations between neighboring pixels in HSV color space. Secondly, the image pixel's texture features, local maximum energy and multiresolution texture gradient, are represented via nonsubsampled contourlet transform. Then, both the pixel-level edge color saliency and texture features 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 human visual attention and local texture content of color image, but also the generalization ability of LS-SVM classifier. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature.
English Abstract: Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. Moreover, the increasing demands of image analysis tasks in terms of segmentation results' quality introduce the necessity of employing multiple cues for improving image-segmentation results. In this paper, we present a least squares support vector machine (LS-SVM) based image segmentation using pixel color-texture descriptors, in which multiple cues such as edge saliency, color saliency, local maximum energy, and multiresolution texture gradient are incorporated. Firstly, the pixel-level edge saliency and color saliency are extracted based on the spatial relations between neighboring pixels in HSV color space. Secondly, the image pixel's texture features, local maximum energy and multiresolution texture gradient, are represented via nonsubsampled contourlet transform. Then, both the pixel-level edge color saliency and texture features 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 human visual attention and local texture content of color image, but also the generalization ability of LS-SVM classifier. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature.
Language: 英语
WOS ID: WOS:000334523200008
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16867
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Yang, Hong-Ying,Zhang, Xian-Jin,Wang, Xiang-Yang. LS-SVM-based image segmentation using pixel color-texture descriptors[J]. PATTERN ANALYSIS AND APPLICATIONS,2014-01-01,17(2):341-359.
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