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图学学报

图学学报

• 视觉与图像 • 上一篇    下一篇

基于扩散的图像显著性检测

  

  1. 1. 中国石油大学(华东)计算机与通信工程学院，山东 青岛 266580；
    2. 中国科学院计算技术研究所智能信息处理重点实验室，北京 100190
  • 出版日期:2017-04-30 发布日期:2017-04-28
  • 基金资助:
    国家自然科学基金项目(61379106，61379082，61227802)；山东省自然科学基金项目(ZR2013FM036，ZR2015FM011，ZR2015FM022)；
    浙江大学CAD&CG 重点实验室开放基金项目(A1315)

Diffusion-Based Image Saliency Detection

  1. 1. College of Computer and Communication Engineering, China University of Petroleum (Huadong), Qingdao Shandong 266580, China;
    2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Online:2017-04-30 Published:2017-04-28

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摘要： 显著性扩散方法是众多显著性检测方法中的一种，此过程中关键是如何更好地选择
种子点并进行有效传播。当图像背景比较复杂尤其是图像的前景和背景颜色接近时，种子点的选
择会出现错误，从而影响最终显著图的质量。从显著性图出发提出一种能够准确找到显著性种子
点的方法，并将随机游走方法改进和流形排序方式融合进行显著性扩散。实验结果表明该方法较
之前方法有了很大的改进，得到了更加精细的显著性图。

关键词: 显著性, 种子点, 扩散, 传播

Abstract: Diffusion-based saliency detection is one of many methods about image saliency detection.
The key is how to choose these seeds and propagate their saliency. Given the unconspicuous
difference of color and contrast between the background and the salient object, the erroneous seeds
may be selected. Finally, these wrong seeds could affect the quality of saliency map. In this paper, we
propose a novel seeds detection approach that take advantage of saliency map. By taking regularized
random walk and manifold ranking into account, the saliency map of an image is then estimated.
Experimental result demonstrates the proposed method performs well and provide pixel-wised
saliency map.

Key words: saliency, seeds, diffusion, propagation