LS-SVM-based image segmentation using pixel color-texture descriptors
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- 作者：Hong-Ying Yang (1)
Xian-Jin Zhang (1)
Xiang-Yang Wang (1) (2) - 关键词：Image segmentation ; Least squares support vector machine ; Human visual attention ; Local texture content ; Arimoto entropy thresholding
- 刊名：Pattern Analysis & Applications
- 出版年：2014
- 出版时间：May 2014
- 年：2014
- 卷：17
- 期：2
- 页码：341-359
- 全文大小：3,314 KB
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Xian-Jin Zhang (1)
Xiang-Yang Wang (1) (2)
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 - ISSN：1433-755X
文摘
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.