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

机译：使用像素颜色纹理描述符的基于LS-SVM的图像分割

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摘要

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 mul-tiresolution 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 contour-let 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.
机译：图像分割仍然是一个重要但难以解决的问题，因为它似乎依赖于应用程序，通常没有关于图像结构的先验信息。此外，就分割结果的质量而言，图像分析任务的需求不断增加，引入了采用多种线索来改善图像分割结果的必要性。在本文中，我们提出了一种使用像素颜色纹理描述符的基于最小二乘支持向量机（LS-SVM）的图像分割方法，其中包括边缘显着性，颜色显着性，局部最大能量和多分辨率纹理梯度等多种线索。合并。首先，基于HSV色彩空间中相邻像素之间的空间关系，提取像素级边缘显着性和色彩显着性。其次，图像像素的纹理特征，局部最大能量和多分辨率纹理梯度通过非下采样轮廓线变换表示。然后，将像素级边缘颜色显着性和纹理特征都用作LS-SVM模型（分类器）的输入，并通过使用Arimoto熵阈值选择训练样本来训练LS-SVM模型（分类器）。最后，用训练有素的LS-SVM模型（分类器）对彩色图像进行分割。这种图像分割不仅可以充分利用人眼的视觉注意力和彩色图像的局部纹理内容，而且可以充分利用LS-SVM分类器的泛化能力。实验结果表明，与文献中最近提出的最新分割方法相比，我们提出的方法具有非常有希望的分割性能。

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  • 来源
    《Pattern Analysis and Applications》 |2014年第2期|341-359|共19页
  • 作者

    Hong-Ying Yang; Xian-Jin Zhang; Xiang-Yang Wang;

  • 作者单位

    School of Computer and Information Technology,Liaoning Normal University, Dalian 116029, China State Key Laboratory of Information Security,Institute of Software, Chinese Academy of Sciences,Beijing 100190, China;

    School of Computer and Information Technology,Liaoning Normal University, Dalian 116029, China State Key Laboratory of Information Security,Institute of Software, Chinese Academy of Sciences,Beijing 100190, China;

    School of Computer and Information Technology,Liaoning Normal University, Dalian 116029, China State Key Laboratory of Information Security,Institute of Software, Chinese Academy of Sciences,Beijing 100190, China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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

    机译：图像分割最小二乘支持向量机;人类的视觉注意力;局部纹理含量;有本熵阈值化;

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