基于纹理抑制和连续分布估计的显著性目标检测方法

邓丹; 吴谨; 朱磊; 刘劲

doi:10.3788/YJYXS20153001.0120

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基于纹理抑制和连续分布估计的显著性目标检测方法

成像技术与图像处理 | 更新时间：2020-08-12

- 基于纹理抑制和连续分布估计的显著性目标检测方法
- Significant target detection method based on texture inhibition and continuous distribution estimation
- 液晶与显示 2015年30卷第1期页码：120-125
- 作者机构：
  
  武汉科技大学信息科学与工程学院,湖北武汉,430081
- 作者简介：
- 基金信息：
  
  湖北省自然科学基金(No.2013CFB333)();高等学校博士学科点专项科研基金(No.20124219120002)();湖北省教育厅科研计划(No.Q20131
- DOI：10.3788/YJYXS20153001.0120
  中图分类号： TP391.4
- 收稿日期：2014-03-04，
  
  修回日期：2014-06-06，
  
  纸质出版日期：2015-02-05
- 稿件说明：
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邓丹, 吴谨, 朱磊, 刘劲. 基于纹理抑制和连续分布估计的显著性目标检测方法[J]. 液晶与显示, 2015,30(1): 120-125

DENG Dan, WU Jin, ZHU Lei, LIU Jing. Significant target detection method based on texture inhibition and continuous distribution estimation[J]. , 2015,30(1): 120-125
邓丹, 吴谨, 朱磊, 刘劲. 基于纹理抑制和连续分布估计的显著性目标检测方法[J]. 液晶与显示, 2015,30(1): 120-125 DOI： 10.3788/YJYXS20153001.0120.

DENG Dan, WU Jin, ZHU Lei, LIU Jing. Significant target detection method based on texture inhibition and continuous distribution estimation[J]. , 2015,30(1): 120-125 DOI： 10.3788/YJYXS20153001.0120.

摘要

为了取得更好的显著性检测结果

针对传统的显著性检测方法易造成边界模糊以及应用中央-周边差进行图像检测时

感兴趣目标的内部纹理会破坏目标的整体性的问题

提出了一种基于纹理抑制和连续分布估计的显著性检测方法.先对图像进行双边滤波的预处理

以平滑目标以及背景区域内部的纹理扰动

保留目标与背景之间的主要边缘.再采用SLIC超像素分割算法

对图像中具有相同特征的像素进行分组

通过多维正态分布提取分割区域的特征

利用二范数Wasserstein距离计算区域相似度:结合局部显著性检测以及全局显著性检测实现目标区域的提取.实验结果表明

本文的方法能够较好地提取显著性目标区域.

Abstract

The traditional methods of saliency detection tend to blur the border and internal texture of interested target

which usually destroys the integrity of the target by applying the center-surround diffe-rences. As to this problem

this paper proposes a saliency detection method based on texture inhibition and continuous distribute estimation in order to obtain better saliency detection results. This paper proposes a saliency detection method based on texture inhibition and continuous distribute estimation. Firstly

bilateral filter is used to smooth texture disturbances of the target and background region

while retaining the major edges between the target and background. Then SLIC superpixel image segmentation algorithms are used to divide the pixels into groups which have lots of pixels with the same characteristics. Then features of segment region are extracted by multidimensional normal distribution

and the similarity of feature saliency is calcula-ted by two norm Wasserstein distance. At last

saliency region can be extracted by the local and global saliency detection. The experiment result shows that the experiment method in this paper can extract saliency region effectively. The experiment results show that our method can obtain better detection result.

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Keywords

references

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