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0.891 8、0.705 2, 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 structured 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 approach. 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.