基于超像素的点互信息边界检测算法

点互信息（PMI）边界检测算法能准确检测图像中的边界，但算法效率受制于采样点的提取。针对采样过程中存在随机性和信息冗余的问题，提出一种利用超像素分割提供的中层结构信息来指导点对选取的方法。首先使用超像素算法对图像进行初始分割，将图像划分成大小形状近似的像素块；然后选取落在相邻超像素中的像素点对，从而使样本点的选取更有目的性，在采样点数目较少时，保证样本点仍能有效完整地获取图像信息。实验通过与原始的PMI边界检测算法在伯克利分割数据库（BSDS）上进行比对验证得出，基于超像素的PMI边界检测算法在采样点对为3500时，平均精准度（AP）达到0.7917，而原始算法则需要6000个同样环境下的采样点对。基于超像素的PMI边界检测算法在保证了检测精度的同时减少了所需的采样点数目，从而能有效提高算法的实时性。

Super-pixel based pointwise mutual information boundary detection algorithm

The Pointwise Mutual Information (PMI) boundary detection algorithm can achieve the boundary of each image accurately, however the efficiency is restricted by the redundancy and randomness of sampling process. In order to overcome the disadvantage, a new method based on the middle structure information provided by super-pixel segmentation was proposed. Firstly, the image was divided into approximately the same super-pixels in the pre-processing. Secondly, the sampling points were located in adjacent different super-pixels which made the sample selection be more ordered, and the image information could still be extracted effectively and completely even though the total number of sampling points was reduced sharply. The comparison experiment of the proposed algorithm and the original PMI boundary detection algorithm was carried out on the Berkeley Segmentation Data Set (BSDS). The results show that the proposed algorithm achieves 0.7917 AP (Average Precision) under PR (Precision/Recall) curve with 3500 sample points, while the original algorithm needs 6000 pairs. It confirms that the proposed algorithm can guarantee the detection accuracy with reducing sample points, which improves the real-time performance effectively.