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Image Segmentation Based on Supernodes and Region Size Estimation

机译:基于超节点和区域大小估计的图像分割

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摘要

A kind of self-adaptive image segmentation algorithm is introduced in this paper, and of which the main frame is based on Graph Structure. Two contributions have been made in our work. First, super-pixels act as the graph nodes for computational efficiency, at the same time, more local features could be abstracted from the pre-segmented image. Second, region size is estimated during the process to reduce interaction between human and computer. Experimental results demonstrate that the improved method is unsupervised and could give satisfactory segmentation.
机译:介绍了一种自适应图像分割算法,该算法的主框架基于图结构。在我们的工作中做出了两点贡献。首先,超像素充当图节点以提高计算效率,同时,可以从预先分割的图像中提取更多局部特征。其次,在处理过程中估计区域大小以减少人与计算机之间的交互。实验结果表明,改进后的方法不受监督,可以给出令人满意的分割效果。

著录项

  • 来源
  • 会议地点 Juan-les-Pins(FR);Juan-les-Pins(FR)
  • 作者

    Yuan Yuan; Lihong Ma; Hanqing Lu;

  • 作者单位

    School of Electronic and Information Engineering, South China Univ. of Tech., 510640 Guangzhou, China;

    School of Electronic and Information Engineering, South China Univ. of Tech., 510640 Guangzhou, China;

    National Lab of Pattern Recognition, Inst. Automation, Chinese Academy of Science, 100080 Beijing, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机网络;
  • 关键词

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