中文摘要：

针对由单一尺度的稀疏重构来描述图像显著性时产生的错误检测问题,提出一种融合上下文信息的多尺度图像显著性检测算法.该方法首先采用多尺度的SLIC超像素分割方法处理输入图片,建立背景模板,采用稀疏表示算法进行稀疏重构;然后构建图像的上下文信息计算各超像素显著值,平滑相似图像块之间的稀疏重构误差,改善前景图像块被错误包含在背景模板时引起的错误检测问题;之后设计加权融合策略完成多尺度显著性融合,最后加入位置信息使得上下文显著性检测的结果更加准确,得到最终的显著图.仿真实验结果表明,在国际公开的数据库中,该方法得到的显著图能够均匀地突出显著对象,较强地抑制背景噪声.

英文摘要：

A multi-scale image saliency detection fusing context information is proposed to overcome the problem of false detection using single scale sparse reconstruction.The original image is first segmented into superpixels using multi-scale SLIC（Simple Linear Iterative Clustering）segmentation algorithm ,and a background template is established using a sparse representation algorithm for sparse reconstruction.Then the image context information is used to calculate the salient value,which helps to smooth similarity between image patches of sparse reconstruction error and to eliminate the false detection that is caused when foreground image blocks are included in the background template.A weighted fusion strategy is designed to complete the multi-scale significant fusion.Finally,the position prior is added to make context saliency detection results more accurate and get the final saliency map.Experimental results show that the proposed algorithm can highlight saliency object uniformly and suppress the background in natural scenes effectively on the public standard salient object detection database.