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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 80-85.doi: 10.6040/j.issn.1672-3961.0.2016.221

• 机器学习与数据挖掘 • 上一篇    下一篇

基于背景和前景交互传播的图像显著性检测

翟继友1,2,周静波1,任永峰2,王志坚2   

  1. 1. 南京工程学院计算机工程学院, 江苏 南京 211167;2. 河海大学计算机与信息学院, 江苏 南京 211100
  • 收稿日期:2016-06-21 出版日期:2017-04-20 发布日期:2016-06-21
  • 作者简介:翟继友(1978— ),男,江苏丰县人,助理研究员,博士研究生,主要研究方向为机器学习与图像分析.E-mail:jiyou1018@126.com
  • 基金资助:
    江苏省高校自然科学研究面上资助项目(14KJB520006),南京工程学院校级科研基金资助项目(CKJA201306)

A visual saliency detection based on background and foreground interaction

ZHAI Jiyou1,2, ZHOU Jingbo1, REN Yongfeng2, WANG Zhijian2   

  1. 1. School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, Jiangsu, China;
    2. College of Computer and Information, Hohai University, Nanjing 211100, Jiangsu, China
  • Received:2016-06-21 Online:2017-04-20 Published:2016-06-21

摘要: 为了更精确地提取图像中的显著性区域,提出一种新的基于背景和前景交互传播的图像显著性检测计算模型。通过建立一个新的模型来寻找图像中的显著性元素,用一种交互式特征传播方法来扩散显著性特征。采用不同参数对图像进行分割,得到多个尺度下的超像素;在单一尺度下通过背景和前景交互传播获得超像素的显著值;对多个显著值进行加权平均融合,并采用平滑机制进行优化得到最终显著图。在公开图像数据库进行的试验结果表明,该模型提高了对图像显著目标大小的适应性,不仅较好地抑制了噪声,还使得显著目标更均匀地凸显出来,结果优于同类的算法。

关键词: 前景, 背景, 交互传播, 显著性检测

Abstract: In order to extract the salient region of image efficiently, a new algorithm model of image saliency detection based on background and foreground interaction was proposed. To find the significant elements in the image, a new model using an interactive feature propagation method to diffuse the significant features was built. The image was segmented into superpixels with different parameters. The salient value of each superpixel was obtained by background and foreground interaction according to a single scale. The final saliency map was obtained by the weighted average fusion of multiple salient values in different scale, and the optimization using the smoothing mechanism. Experimental results showed that the proposed method performed better than the other state-of-the-art methods, which improved the adaptability to the size of salient regions. In addition, our method was proved better not only in restraining the noise, but also in making the salient objects more uniform.

Key words: background, foreground, interactive propagation, saliency detection

中图分类号: 

  • TP301.6
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