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基于深度学习的图像显著区域检测

Salient Region Detection of Images Based on Deep Learning

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摘要

对区域的边界和物体边缘像素使用聚焦操作来计算区域显著特征,采用全局颜色显著性计算全局显著特征,基于卷积神经网络(CNN)融合区域显著特征和全局显著特征,获得最终的显著图,同时采用循环结构网络,多次参考周围环境信息,剔除噪声特点。在MSRA图像库和ECSSD图像库中测试所提算法,其平均精度和平均召回的调和平均值、平均误差均优于当前流行算法。

Abstract

The prominent features of the salient region are determined by focusing on the regional boundary and the object′s edge pixels. Further, the uniqueness of the salient global color is used to calculate global features. Finally, the salient region is obtained using the convolutional neural network (CNN) model based on the regional and global salient features. Adopting a circular structure network is critical to eliminate the noise characteristics by referring to the surrounding environment information for multiple times. The proposed algorithm is tested using the image libraries of MSRA and ECSSD and it is found that its harmonic mean and average error associated with the average precision and recall are better than those of the current popular algorithms.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.4

DOI:10.3788/lop56.091007

所属栏目:图像处理

基金项目:国家自然科学基金(51177115)、陕西省自然科学基础研究计划(2017JQ6054)、陕西省重点科技创新团队计划(2014KCT-16)、陕西省科学技术研究发展计划项目(2014XT-07)、陕西省工业科技攻关项目(2015GY-075)、西安工程大学博士启动基金(BS1505)

收稿日期:2018-11-08

修改稿日期:2018-11-12

网络出版日期:2018-12-06

作者单位    点击查看

纪超:西安工程大学电子信息学院, 陕西 西安 710048
黄新波:西安工程大学电子信息学院, 陕西 西安 710048
曹雯:西安工程大学电子信息学院, 陕西 西安 710048
朱永灿:西安工程大学电子信息学院, 陕西 西安 710048
张烨:西安工程大学电子信息学院, 陕西 西安 710048

联系人作者:纪超(dachao9898@163.com)

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引用该论文

Ji Chao,Huang Xinbo,Cao Wen,Zhu Yongcan,Zhang Ye. Salient Region Detection of Images Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091007

纪超,黄新波,曹雯,朱永灿,张烨. 基于深度学习的图像显著区域检测[J]. 激光与光电子学进展, 2019, 56(9): 091007

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