结合稀疏重构与能量方程优化的显著性检测

s extant method wrongly highlights backgrounds in salient object detection from complex background images, this paper proposed a new algorithm of saliency detection to suppress background in combination with sparse reconstruction and energy optimization, which extracted foreground more accurate instantaneously. Firstly, it decomposed the input image into superpixel within Abstract: ng unnecessary detail. Then, it selected image boundary superpixels as background templates, which used as sparse dictionaries to calculate reconstruction errors that as superpixels initial saliency. Finally, it introduced the energy equation to optimize the initial saliency, and generated the final saliency map after the foreground of optimized saliency was enhanced. It tested the proposed algorithm and other 10 algorithms on MSRA10K and ECSSD1000 dataset with ground truths. The PR Curve, Precision (P) and F-measure(F) of the proposed algorithm had better performance than other 10 algorithms. The experimental results show that the proposed algorithm is more robust to suppress background effectively, and the extraction of salient object is more precise.