基于图理论的半监督图像分割与应用研究

项目名称： 基于图理论的半监督图像分割与应用研究

项目编号： No.61472173

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 其他

项目作者： 黄志开

作者单位： 南昌工程学院

项目金额： 82万元

中文摘要： 图像分割是图像处理中重要的基础环节，它将像素分隔成有意义的区域。基于图的半监督学习是半监督学习领域的主流方法之一。本项目以图理论、域变换、谱聚类以及多尺度的结构张量分析为工具，探讨基于图理论的半监督学习的图像分割算法。从数据分析的角度，充分将流形学习和半监督学习的思想相结合，综合考虑图像数据的局部和全局拓扑结构，构造样本的邻接矩阵，探讨冗余特征以及维数对图上距离测度的影响，结合特征子集进行随机采样的随机子空间方法，建立一种适用于图像分割的局部自适应图模型；研究一种基于域变换的整合全局和局部拓扑结构的半监督维数约减方法。以提高计算效率为目的，研究多尺度图理论的半监督学习的图像分割算法。同时对稀疏相似矩阵计算、结构和约束保持的半监督图像特征选择、约束能量函数的半监督流形学习算法以及谱聚类的图像分割算法等方面展开研究。本项目的深入研究，将对图像处理与计算机视觉学科等的发展起到一定的推动作用。

中文关键词： 图像分割；图；半监督学习；谱聚类；流形学习

英文摘要： As one of the most important and typical problems in image processing and computer vision fields, image segmentation by which the pixel is divided into visually meaningful regions is the basic premise in image vision analysis and pattern recognition. In recent years, as a branch of the semi-supervised learning, graph-based semi-supervised learning has drawn more and more attention since graph-based semi-supervised learning is very important to improve the performance of machine learning system. In this project, image segmentation algorithm of graph theory based semi-supervised learning has been explored by using graph theory, Gabor transform, spectral structure tensor clustering combined with multi-scale analysis as a tool. Based on a comprehensive data analysis, we shall resort to the idea such as manifold learning and semi-supervised learning, combine local and global topological structure, consider the image data set, construct the adjacency matrix structure sample, investigate the effect of redundant features and noise as well as the dimension of the distance measure on graphs, and random subspace method of random sampling for the feature subset of image, In addition, we shall establish a local adaptive graph model which is suitable for color image segmentation. Further, a semi-supervised dimensionality reduction method of integration domain transform global and local topology will be proposed. In order to improve the computational efficiency of graph, we shall study image segmentation method for semi-supervised learning based on multi-scale analysis. At the same time，some researched will be carried out for image segmentation，such as sparse similarity matrix calculation, graph based structure and constraints, the semi-supervised feature selection method, semi-supervised manifold learning algorithm, constrained energy function, spectral clustering method. By means of the exhaustive research for this project, the discipline such as image processing, computer vision, etc will be further promoted and developed.

英文关键词： Image Segmentation;Graph;Semi-Supervised Learning;Spectral Clustering;Manifold Learning