融合高低层多特征的显著性检测算法

孙君顶; 张毅; 李海华

doi:10.3788/YJYXS20193404.0430

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融合高低层多特征的显著性检测算法

图像处理 | 更新时间：2020-07-05

- 融合高低层多特征的显著性检测算法
- Saliency detection algorithm integrating multiple features of high and low level
- 液晶与显示 2019年34卷第4期页码：430-438
- 作者机构：
  
  1.河南理工大学计算机科学与技术学院, 焦作 454000
  2.河南理工大学物理与电子信息学院, 焦作 454000
- 作者简介：
  
  [ "孙君顶(1975-), 男, 河南邓州人, 博士、教授, 2008年于西安电子科技大学获得博士学位, 主要从事图像处理与模式识别的研究。E-mail:sunjunding1975@sina.com" ]
- 基金信息：
  
  国家自然科学基金项目(61602157);National Natural Science Foundation of China(61602157)
- DOI：10.3788/YJYXS20193404.0430
  中图分类号： TP391
- 收稿日期：2018-10-17，
  
  录用日期：2019-1-8，
  
  纸质出版日期：2019-04
- 稿件说明：
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孙君顶, 张毅, 李海华. 融合高低层多特征的显著性检测算法[J]. 液晶与显示, 2019,34(4):430-438.

Jun-ding SUN, Yi ZHANG, Hai-hua LI. Saliency detection algorithm integrating multiple features of high and low level[J]. Chinese journal of liquid crystals and displays, 2019, 34(4): 430-438.
孙君顶, 张毅, 李海华. 融合高低层多特征的显著性检测算法[J]. 液晶与显示, 2019,34(4):430-438. DOI： 10.3788/YJYXS20193404.0430.

Jun-ding SUN, Yi ZHANG, Hai-hua LI. Saliency detection algorithm integrating multiple features of high and low level[J]. Chinese journal of liquid crystals and displays, 2019, 34(4): 430-438. DOI： 10.3788/YJYXS20193404.0430.

摘要

针对单一显著性特征无法全面表达图像显著性致使显著性检测精度不高等问题，本文提出了一种多特征融合的显著性检测算法。算法在高层先验知识基础上，对靠近中心的超像素设置高显著值，利用高斯分布求解中心先验；在底层特征上融合图像的边界稀疏、全局对比度、颜色空间分布和超级像素差异等4种显著特征，利用类间差异最大阈值对高低层特征进行线性和非线性融合，最终得到高质量的显著图。在MSRA-1000、SED、SOD 3个公开的数据集上进行实验，结果表明：本文算法融合得到的显著图边缘清晰、显著区域突出均匀，在有效抑制背景信息的同时所得显著图像视觉感知更好，与其他显著性算法相比查全率和查准率上至少提高3.4%。

Abstract

Aiming at the problem that the single saliency feature can not fully express the image saliency

resulting in low accuracy of saliency detection

a saliency detection algorithm for multi-feature fusion was proposed. Based on the high-level prior knowledge

the background priori and the center priori were redefined.The super-pixels near the center were set to high saliency values

and the center priori was solved by using Gauss distribution. Four kinds of salient features such as image sparseness

global contrast

color space distribution and super pixel difference on the underlying features were fused

and the maximum threshold and high-low layer features of the inter-class difference were linearly and nonlinearly fused to obtain a high-quality saliency map. Experiments on three public datasets of MSRA-1000

SED and SOD show that the saliency map obtained by the fusion of the algorithm has clear edges and prominent regions

and the visual image perception is better when the background information is effectively suppressed. Compared with other significant algorithms

the recall rate and precision rate are increased by at least 3.4%.

关键词

Keywords

references

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