基于协同表征的二部图矿石图像分割  

Ore Image Segmentation by Bipartite Graph Based on Collaborative Representation

作　　者：周静[1] 杨凡[1] 史凌祎 郑忠龙[1] 

机构地区：[1]浙江师范大学数理与信息工程学院,浙江金华321004

出　　处：《计算机工程》2016年第10期236-241,共6页Computer Engineering

基　　金：国家自然科学基金资助项目(61170109);浙江省自然科学基金资助项目(LY14F030022;LY13F020015);浙江省科技厅基金资助项目(2015C31095)

摘　　要：二部图的图像分割算法同时考虑到超像素之间、像素与超像素之间的空间组织关系,对矿石图像分割具有较好的鲁棒性。在二部图的构造过程中,引入■_0稀疏表征识别方法,保证全局特性和语义分割结果,但增加了算法的复杂度,使运算开销过大。为此,提出一种基于协同表征的二部图图像分割算法,该算法在保证全局特性的同时考虑超像素之间的局部信息,对于色彩单一、碎片重叠、粘连的矿石图像分割鲁棒性较好。结合协同表征,在保证分割效果的同时,解决■_0范数造成的复杂度过高问题。对不同分割算法的仿真实验结果验证了该算法的有效性。Image segmentation algorithm by bipartite graph considers the spatial organization relation between superpixels as well as pixel and superpixels,which is robust for ore image segmentation. This paper proposes a bipartite graph algorithm based on Collaborative Representation （CR）, which is able to ensure global features and local information. It takes image segmentation as a bipartite graph partitioning problem and uses a superpixel segmentation to search for the most probable groups of superpixels. CR method can reduce the complexity of 0 normalized image segmentation algorithm. Besides it is robust for segmenting ore images which have monotonic color changes and overlapping fragments, and compares to different segmentation algorithm. Simulation results of different segmentation algorithms show the validity of the proposed algorithm.

关 键 词：图像分割 超像素 协同表征 二部图 谱聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]