电子学报 ›› 2019, Vol. 47 ›› Issue (6): 1352-1365.DOI: 10.3969/j.issn.0372-2112.2019.06.024
钱晓亮1, 白臻1, 陈渊1, 张鼎文2, 史坤峰1, 王芳1, 吴青娥1, 毋媛媛1, 王慰1
收稿日期:
2018-05-02
修回日期:
2018-08-02
出版日期:
2019-06-25
通讯作者:
作者简介:
基金资助:
QIAN Xiao-liang1, BAI Zhen1, CHEN Yuan1, ZHANG Ding-wen2, SHI Kun-feng1, WANG Fang1, WU Qing-e1, WU Yuan-yuan1, WANG Wei1
Received:
2018-05-02
Revised:
2018-08-02
Online:
2019-06-25
Published:
2019-06-25
Supported by:
摘要: 协同视觉显著性检测是视觉注意力计算领域中一个快速发展的新兴分支,致力于检测多幅相关场景图像中的公共显著目标,在各种计算机视觉任务中有广泛应用.考虑到特征提取策略的设计是协同视觉显著性检测当前研究的重点,本文首先根据特征提取策略的不同对现有的协同视觉显著检测方法进行了分类介绍和定性分析.其次,通过在5个公开数据库上的主观和定量对比,对各流行算法的性能进行了评估,分析了特征提取策略对算法性能的影响以及各数据库的复杂度,并验证了协同视觉显著性检测和视觉显著性检测的区别.最后,对本文工作进行了总结,并对当前研究中存在的问题和未来的研究工作进行了讨论.
中图分类号:
钱晓亮, 白臻, 陈渊, 等. 协同视觉显著性检测方法综述[J]. 电子学报, 2019, 47(6): 1352-1365.
QIAN Xiao-liang, BAI Zhen, CHEN Yuan, et al. A Review of Co-saliency Detection[J]. Acta Electronica Sinica, 2019, 47(6): 1352-1365.
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