[1]杨麟,杜吉祥,聂一亮.块聚类的协同显著性检测[J].华侨大学学报(自然科学版),2018,39(3):445-450.[doi:10.11830/ISSN.1000-5013.201706027]
 YANG Lin,DU Jixiang,NIE Yiliang.Co-Saliency Detection Using Patch-Cluster[J].Journal of Huaqiao University(Natural Science),2018,39(3):445-450.[doi:10.11830/ISSN.1000-5013.201706027]
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块聚类的协同显著性检测()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第3期
页码:
445-450
栏目:
出版日期:
2018-05-20

文章信息/Info

Title:
Co-Saliency Detection Using Patch-Cluster
文章编号:
1000-5013(2018)03-0445-06
作者:
杨麟 杜吉祥 聂一亮
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
YANG Lin DU Jixiang NIE Yiliang
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
协同显著性检测 协同分割 块聚类 显著性测度 测度融合
Keywords:
co-saliency detection co-segmentation patch clustering saliency cue cue fused
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201706027
文献标志码:
A
摘要:
针对复杂背景的多图像显著性检测问题,提出一种基于块聚类的多图像协同显著性检测方法.该方法通过构建多图像间共同对象的关联性,利用块聚类计算4种显著性测度并融合,获得较好的协同显著性检测效果.实验结果表明:基于块聚类的协同显著性检测方法能够有效提高检测精度,且鲁棒性较高.
Abstract:
For the co-saliency detection problem in multiple images with complex background, this paper put forward a co-saliency detection method for multiple images which based on patch-cluster. This method constructing the correlation of the common objects between images, and using block clustering to calculate four kinds of saliency measurement and fused to get a better co-saliency detection effect. Experimental results show that the co-saliency detection method which based on patch-cluster can effectively improve the detection accuracy, and the have higher robustness.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2017-06-12
通信作者: 杜吉祥(1977-),男,教授,博士,主要从事模式识别与图像处理的研究.E-mail:jxdu@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61673186, 61370006, 61502183); 福建省自然科学基金资助项目(2013J06014, 2014J01237); 华侨大学中青年教师科研提升资助计划项目(ZQN-YX108); 华侨大学研究生科研创新培育计划资助项目(1511314025)
更新日期/Last Update: 2018-05-20