一种改进超像素融合的图像分割方法

基于超像素的传统图像分割方法在边缘分割的一致性、计算效率和融合算法的自适应性等方面仍存在诸多问题. 文章结合国内外相关研究进展，提出了一种新型超像素融合的图像分割方法. 方法采用ERS超像素过分割算法，以强度、梯度直方图作为超像素特征，并采取EMD方法计算特征距离，通过混合Weibull模型获取融合自适应阈值，进而完成分割. 算法时间复杂度降至为O(N)，分割过程中不需要手动选取待分割区域，有效提高了算法的自适应性. 实验结果表明本方法在分割边界准确度和处理效率方面优于现有方法.

An Image Segmentation Approach with Progressive Superpixel Merging

Traditional image segmentation methods based on superpixel still have many problems in terms of consistency of edge segmentation, computational efficiency and adaptability of merging algorithms. We combine domestic and foreign research advances and propose a novel superpixel merging image segmentation method, which adopts ERS superpixel over-segmentation algorithm and uses intensity and gradient histogram as superpixel features. Additionally, EMD method is used to calculate feature distance and the merging self-adaptive threshold is obtained by mixing Weibull model to complete the segmentation. As a result, the time complexity of proposed algorithm is reduced to O(N), and the segmentation process is not required to manually select the region to be segmented. Compared with current methods, experiment results show that the proposed method has better performance on boundary accuracy and processing efficiency.