[1]赵闰霞,蹇木伟,齐强,等.基于Object Proposals并集的显著性检测模型[J].智能系统学报,2018,13(06):946-951.[doi:10.11992/tis.201801009]
 ZHAO Runxia,JIAN Muwei,QI Qiang,et al.Saliency detection model based on the union of Object Proposals[J].CAAI Transactions on Intelligent Systems,2018,13(06):946-951.[doi:10.11992/tis.201801009]
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基于Object Proposals并集的显著性检测模型(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年06期
页码:
946-951
栏目:
出版日期:
2018-10-25

文章信息/Info

Title:
Saliency detection model based on the union of Object Proposals
作者:
赵闰霞1 蹇木伟12 齐强1 王静1 王瑞红1 董军宇1
1. 中国海洋大学 信息科学与工程学院, 山东 青岛 266000;
2. 山东财经大学 计算机科学与技术学院, 山东 济南 250014
Author(s):
ZHAO Runxia1 JIAN Muwei12 QI Qiang1 WANG Jing1 WANG Ruihong1 DONG Junyu1
1. College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China;
2. School of Computer Science & Technology, Shandong University of Finance and Economics, Ji’nan 250014, China
关键词:
显著性检测object proposal超像素纹理背景图全局对比度边界连通性自底向上
Keywords:
saliency detectionObject Proposalsuperpixelstexturebackground mapglobal contrastboundary connectivitybottom-up
分类号:
TP391
DOI:
10.11992/tis.201801009
摘要:
针对当前常见的显著性检测模型得到的结果会包含大量的背景区域的缺点,本文提出了基于Object Proposals并集的显著性检测模型。该模型首先对于输入图片生成一系列Object Proposals,并通过其并集计算得到背景图;然后结合纹理特征和全局对比度得到初始显著图;最后,用得到的背景图对初始显著图进行背景抑制得到最终显著图。实验结果表明,在通用MSRA1000数据集上,本文提出的显著性模型与其他5种方法相比取得了很好的效果。
Abstract:
In saliency detection, current existing models usually produce results containing many background regions. To improve the performance, a novel saliency detection model is proposed based on the union of object proposals. The model first generates a series of object proposals from the input pictures, and then gets the background map by computing the union, and then obtains the initial saliency map by combining the texture and global contrast. Finally, the final saliency map is derived by restraining the initial saliency map with the obtained background map. Experimental results on the general MSRA1000 dataset demonstrate that the proposed saliency model performs well compared to the other five existing methods.

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备注/Memo

备注/Memo:
收稿日期:2018-01-08。
基金项目:国家自然科学基金项目(61601427,61602229).
作者简介:赵闰霞,女,1993年生,硕士研究生,主要研究方向为图像处理;蹇木伟,男,1982年生,教授,博士生导师,CCF计算机视觉专委会委员,CCF多媒体专委会委员,CCF机器学习与模式识别通讯委员,山东数媒专委会委员等。主要研究方向为图像处理、模式识别、多媒体计算、机器学习、认知科学。主持国家自然科学基金等研究课题10余项。以第一发明人或第一申请人被授予3项国家专利,其中1项国家发明专利和2项国家实用新型专利。发表学术论文50余篇。被SCI检索的国际期刊论文14篇、被EI检索论文40余篇;齐强,男,1990年生,硕士研究生,主要研究方向为图像处理、模式识别、水下视觉。
通讯作者:蹇木伟.E-mail:20173016@sdufe.edu.cn
更新日期/Last Update: 2018-12-25