融合前景信息的背景模板优化显著性检测算法

Background Template Optimization Saliency Detection Algorithm Fusing with Foreground Information

当显著目标位于图像边界时,已有显著性检测模型往往将显著性区域误检为背景模板,导致检测效果不佳。为此,提出一种背景模板优化的显著性检测算法。设计一种选择策略移除图像边界区域的显著超像素模块,建立改进的背景模板后计算基于背景的显著图,从该显著图中得到紧凑的前景区域,描述显著目标的外观和位置后计算基于前景的显著图。在此基础上,将2个显著图进行融合,通过一种能量函数对其进行改善,得到最终平滑和精确的显著图。实验结果表明,相对SEG、CA等算法,该算法能提高目标检测的精确率与召回率。

When salient objects exist at image boundaries,most saliency detection models misdetect salient regions as background templates,resulting in poor detection results.To solve this problem,a saliency detection algorithm based on background template optimization is proposed.A selection strategy is designed to remove the salient super-pixel module in the image boundary area,and an improved background template is established to calculate the salient image based on the background.A compact foreground region is obtained from the saliency map,and the saliency map based on foreground is calculated after describing the appearance and location of salient objects.On this basis,two saliency maps are fused and improved by an energy function,and eventually more smooth and accurate saliency maps are obtained.Experimental results show that the proposed algorithm can improve the accuracy and recall rate of target detection compared with SEG,CA and other algorithms.