基于稀疏恢复与优化的显著性目标检测算法

针对目前基于稀疏表示的显著性检测算法中存在的边界显著性检测不足、字典表达能力不够等问题,提出一种基于稀疏恢复与优化的检测算法。首先对图像进行滤波平滑和超像素分割,并从边界与内部超像素中挑选可靠的背景种子构建稀疏字典；然后基于该字典对整幅图像进行稀疏恢复,根据稀疏恢复误差生成初始显著图；再运用改进的基于聚类的二次优化模型对初始显著图进行优化；最后经过多尺度融合得到最终显著图。在三大公开测试数据集上的实验结果表明,所提算法能够保持高效快速、无训练等优点,同时性能优于目前主流的非训练类算法,在处理边界显著性方面表现优异,具有较强的鲁棒性。

Salient Object Detection Algorithm Based on Sparse Recovery and Optimization

In view of the issues of boundary ambiguity and low detection accuracy in current saliency detection algorithms which employ sparse representation,this paper proposed a new saliency detection algorithm based on sparse recovery and optimization.Firstly,the RG filter is used to smooth the image.Then,the SLIC algorithm is used to segment the image,and the reliable background seed is selected from the boundary and the inside super pixel block is chosen to construct the dictionary.Based on the dictionary,the sparse recovery of the whole image is achieved,and the initial sa-liency map is generated according to the sparse recovery error.After that,the modified optimization model is used to optimize the initial saliency map.Finally,the final saliency map is obtained through multiscale fusion.Experimental results on three public benchmark datasets show that the performance of the proposed algorithm is superior to the current state-of-the-art methods.Meanwhile,it performs well in dealing with boundary saliency and has strong robustness.