Pattern Analysis and Applications

, Volume 17, Issue 2, pp 341–359 | Cite as

LS-SVM-based image segmentation using pixel color-texture descriptors

Theoretical Advances
First Online:
Received:
Accepted:

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.

Keywords

Image segmentation Least squares support vector machine Human visual attention Local texture content Arimoto entropy thresholding 
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Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61272416, 60773031 and 60873222, the Open Foundation of State Key Laboratory of Information Security of China under Grant No. 04-06-1, the Open Foundation of Network and Data Security Key Laboratory of Sichuan Province, the Open Foundation of Key Laboratory of Modern Acoustics Nanjing University under Grant No. 08-02, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. 2008351 and L2010230.

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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Hong-Ying Yang
    • 1
  • Xian-Jin Zhang
    • 1
  • Xiang-Yang Wang
    • 1
    • 2
  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianChina
  2. 2.State Key Laboratory of Information Security, Institute of SoftwareChinese Academy of SciencesBeijingChina

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