Salient Object Detection via Recursive Sparse Representation
Salient Object Detection via Recursive Sparse Representation
复制标题
通过递归稀疏表示进行显着目标检测
DOI：
10.3390/rs10040652
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发表时间：
2018-04
期刊：
影响因子：
5
通讯作者：
Li Yansheng
中科院分区：
文献类型：
--
作者：
Zhang Yongjun;Wang Xiang;Xie Xunwei;Li Yansheng
目标级显著性检测是一个非常有吸引力的研究领域，它对许多基于内容的计算机视觉和遥感任务都很有用。本文从递归稀疏表示的角度介绍了一种有效的无监督显著目标检测方法。利用前景和背景字典确定的重建误差作为显著性指标，有效地改善了目标完整性的不足。该方法包括以下四个步骤：(1)区域特征提取；(2)根据初始显著性图和图像边界约束提取背景和前景字典；(3)稀疏表示和显著性度量；(4)递归处理，使用当前显著性映射更新步骤2中的初始显著性映射并重复步骤3。本文还利用3个基准数据集以及部分卫星和无人机遥感图像与7种最先进的显著性检测方法进行了实验对比，验证了所提方法比现有方法更有效，在多目标检测和保持目标区域完整性方面都能取得更好的性能。
Object-level saliency detection is an attractive research field which is useful for many content-based computer vision and remote-sensing tasks. This paper introduces an efficient unsupervised approach to salient object detection from the perspective of recursive sparse representation. The reconstruction error determined by foreground and background dictionaries other than common local and global contrasts is used as the saliency indication, by which the shortcomings of the object integrity can be effectively improved. The proposed method consists of the following four steps: (1) regional feature extraction; (2) background and foreground dictionaries extraction according to the initial saliency map and image boundary constraints; (3) sparse representation and saliency measurement; and (4) recursive processing with a current saliency map updating the initial saliency map in step 2 and repeating step 3. This paper also presents the experimental results of the proposed method compared with seven state-of-the-art saliency detection methods using three benchmark datasets, as well as some satellite and unmanned aerial vehicle remote-sensing images, which confirmed that the proposed method was more effective than current methods and could achieve more favorable performance in the detection of multiple objects as well as maintaining the integrity of the object area.
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DOI：
10.1007/978-3-319-58961-9_1
发表时间：
2017-05
期刊：
--
影响因子：
--
作者：
Zhouqin He;Bo Jiang;Yun Xiao;C. Ding;B. Luo
通讯作者：
Zhouqin He;Bo Jiang;Yun Xiao;C. Ding;B. Luo
影响因子：
6
作者：
Hong Li;E. Wu;Wen Wu
通讯作者：
Hong Li;E. Wu;Wen Wu
DOI：
10.1109/tpami.2011.272
发表时间：
2012-10-01
影响因子：
23.6
作者：
Goferman, Stas;Zelnik-Manor, Lihi;Tal, Ayellet
通讯作者：
Tal, Ayellet
DOI：
10.1109/cvpr.2009.5206596
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
通讯作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
影响因子：
5
作者：
Dong, Chao;Liu, Jinghong;Xu, Fang
通讯作者：
Xu, Fang