Institutional Repository
| LS-SVM-based image segmentation using pixel color-texture descriptors | |
| Yang, Hong-Ying; Zhang, Xian-Jin; Wang, Xiang-Yang | |
| 2014 | |
| Source | PATTERN ANALYSIS AND APPLICATIONS
 |
| ISSN | 1433-7541 |
| Volume | 17Issue:2Pages:341-359 |
| 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.; 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. |
| Indexed Type | SCI |
| Keyword | Image Segmentation Least Squares Support Vector Machine Human Visual Attention Local Texture Content Arimoto Entropy Thresholding |
| 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. |
| Language | 英语 |
| WOS ID | WOS:000334523200008 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16867 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Yang, Hong-Ying,Zhang, Xian-Jin,Wang, Xiang-Yang. LS-SVM-based image segmentation using pixel color-texture descriptors[J]. PATTERN ANALYSIS AND APPLICATIONS,2014,17(2):341-359. |
| APA | Yang, Hong-Ying,Zhang, Xian-Jin,&Wang, Xiang-Yang.(2014).LS-SVM-based image segmentation using pixel color-texture descriptors.PATTERN ANALYSIS AND APPLICATIONS,17(2),341-359. |
| MLA | Yang, Hong-Ying,et al."LS-SVM-based image segmentation using pixel color-texture descriptors".PATTERN ANALYSIS AND APPLICATIONS 17.2(2014):341-359. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment