摘要
显著性物体检测的关键在于准确地突出前景区域,多数传统方法在处理复杂背景图像时效果不理想。针对上述问题,提出了一种基于前景增强与背景抑制的显著性物体检测方法。首先,利用简单线性迭代聚类(SLIC)将图像进行分割得到多个超像素区域,通过区域间的对比和边界信息分别获得图像的显著区域与背景种子,并通过计算得到基于区域间对比和基于背景的两幅显著图。然后,在两幅图像中运用Seam Carving和Graph-based的图像分割法区分显著与非显著区域,进而得到前景增强与背景抑制模板。最终,融合两幅显著图与模板得到最终的显著图。在公开数据集MSRA-1000上对算法进行验证,结果表明,所提算法与7种主流算法相比具有更好的查准率和查全率。
Salient object detection is an important tool for highlighting the foreground region accurately,however most traditional algorithms do not work well when dealing with complex background images.We thus propose a novel salient object detection method based on foreground enhancing and background suppressing.For acquiring multiple super-pixel regions,we segment the source image by the simple linear iterative clustering（SLIC）,and the salient regions and the seed background of the image can be obtained via region contrast and boundary information,based on which the region-contrastbased and background-based salient images are achieved.Then we distinguish salient regions from nonsalient regions in the two salient images using the seam carving and graph-based image segmentation,which is helpful for obtaining the foreground enhancing and background suppressing template.Furthermore,we integrate the salient images and template as the final salient image.Experimental results which are applied to a public benchmark dataset（MSRA-1000）show that the proposed algorithm has better precision and recall ratio than the seven classical algorithms.
作者
王豪聪
赵晓叶
彭力
WANG Hao-cong;ZHAO Xiao-ye;PENG Li(Engineering Research Center of Internet of Things Technology Applications of the Ministry of Education;School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)
出处
《计算机工程与科学》
CSCD
北大核心
2018年第6期1119-1124,共6页
Computer Engineering & Science
基金
国家自然科学基金(61203147
61374047
61403168)
江苏省产学研联合创新基金资助-前瞻性联合研究项目(BY2014023-25)
关键词
显著性物体检测
超像素
前景增强
背景抑制
salient object detection
super pixel
foreground enhancing
background suppressing
