基于结构标签学习的显著性目标检测

摘要

提出了一种基于结构标签学习的显著性目标检测算法,将结构化学习方法应用到显著性目标检测中.首先从含有标记的图像中随机采集固定大小的矩形区域,并记录其结构标签；然后使用含结构标签的区域特征构建决策树集合；最后采用监督学习的方法对图像进行优化预测,得到最终的显著图.实验结果表明,本文方法能较准确地检测出图像库中图像的显著性区域,在数据库 MSRA5000和 BSD300的 AUC 值分别为0．8918、0．7052,说明本文方法具有较好的显著性检测效果.%This paper proposes a salient object detection method based on structured labels learning, applying a structured learning method to salient object detection.Firstly,we get a fixed rectangular region randomly from the local image which includes the labeling,and record the corresponding struc-tured labels.Then,a collection of decision trees is built by using the regional features which includes the structured labels.Finally,the final saliency map is captured by using the supervised learning ap-proach.Experiments show that our method can detect the salient objects accurately,and the AUC scores are 0.891 8 and 0.705 2 on the MSRA5000 and BSD300 datasets,the result shows that our method can achieve good effect in salient object detection.