基于双重超像素集的快速路径相似度图像分割算法

为克服基于路径相似度计算时间复杂度高以及基于单一过分割区域集的聚类方法 容易导致误合并的缺陷, 提出一种结合均值漂移和路径相似度的谱聚类算法. 该算法使用超像 素构建基于路径相似度的模型来实现加速. 首先, 利用均值漂移算法对图像进行两次预分割(不同参数), 将这些过分割区域视为两组超像素集合, 构建基于双重过分割区域集的加权图; 之后, 使用各超像素的色彩均值和超像素间存在的交叉像素计算初始相似度, 再利用路径相似度模型得 到基于路径的相似度; 最后, 采用Multiway Ncut算法进行聚类. 通过算法自身参数和图结构实验, 测试算法的鲁棒性和稳定性; 通过多幅彩 色图片的分割实验, 表明本文的方法在准确性和时效性方面都具有很好的性能.

A Fast Image Segmentation Based on Path-based Similarity and Dual Super-pixel Sets

Path-based clustering is a recently developed clustering approach that has delivered impressive results in quite a few challenging tasks. However, its extremely high computational complexity limits the application to image segmentation. In this paper, we propose a fast path-based spectral clustering method by defining a dual region-based graphical model for similarity computation. Our method is significantly faster than path-based clustering for considering the over-segmented regions generated from the mean shift algorithm as graph nodes, whose number is much less than that of the image pixels. Besides, taking over-segmented regions as nodes may reduce the sensitivity to noise and outliers. Furthermore, the graphical model combined double segmentations in a principle manner to avoid inappropriate partition which often occurs in a single region-based graphical model. We have performed experiments under both unsupervised and semi-supervised settings, and compared our method with some other methods as well. Experimental results show that our method consistently outperforms other methods due to its great accuracy and lower computational complexity.