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Top-Down Visual Saliency Detection in Optical Satellite Images Based on Local Adaptive Regression Kernel
Cui, Xiaoguang; Tian, Yuan
发表期刊Journal of Multimedia
2014
卷号9期号:1页码:173-180
摘要This paper proposes a novel top-down visual
saliency detection method for optical satellite images using
local adaptive regression kernels. This method provides a
saliency map by measuring the likeness of image patches to
a given single template image. The local adaptive regression
kernel (LARK) is used as a descriptor to extract feature and
compare against analogous feature from the target image.
A multi-scale pyramid of the target image is constructed to
cope with large-scale variations. In addition, accounting for
rotation variations, the histogram of kernel orientation is
employed to estimate the rotation angle of image patch, and
then comparison is performed after rotating the patch by the
estimated angle. Moreover, we use the bounded partial correlation
(BPC) to compare features between image patches
and the template so as to rapidly generate the saliency map.
Experiments were performed in optical satellite images to
find airplanes, and experimental results demonstrate that the
proposed method is effective and robust in complex scenes.
关键词Visual Saliency Top-down Kernel Regression Rotation Invariance Bounded Partial Correlation
收录类别EI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12414
专题空天信息研究中心
通讯作者Cui, Xiaoguang
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Cui, Xiaoguang,Tian, Yuan. Top-Down Visual Saliency Detection in Optical Satellite Images Based on Local Adaptive Regression Kernel[J]. Journal of Multimedia,2014,9(1):173-180.
APA Cui, Xiaoguang,&Tian, Yuan.(2014).Top-Down Visual Saliency Detection in Optical Satellite Images Based on Local Adaptive Regression Kernel.Journal of Multimedia,9(1),173-180.
MLA Cui, Xiaoguang,et al."Top-Down Visual Saliency Detection in Optical Satellite Images Based on Local Adaptive Regression Kernel".Journal of Multimedia 9.1(2014):173-180.
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