ANALYSIS OF GRAPH CLUSTERING BASED NORMALIZED GRAPH CUT FOR IMAGE SEGMENTATION

Image Segmentation Using Quadtree-Based Similarity Graph and Normalized Cut

The graph cuts in image segmentation have been widely used in recent years because it regards the problem of image partitioning as a graph partitioning issue, a well-known problem in graph theory. The normalized cut approach uses spectral graph properties of the image representative graph to bipartite it into two or more balanced subgraphs, achieving in some cases good results when applying thi...

Multiregion Graph Cut Image Segmentation

The purpose of our study is two-fold: (1) investigate an image segmentation method which combines parametric modeling of the image data and graph cut combinatorial optimization and, (2) use a prior which allows the number of labels/regions to decrease when the number of regions is not known and the algorithm initialized with a larger number. Experimental verification shows that the method resul...

GANC: Greedy agglomerative normalized cut for graph clustering

This paper describes a graph clustering algorithm that aims to minimize the normalized cut criterion and has a model order selection procedure. The performance of the proposed algorithm is comparable to spectral approaches in terms of minimizing normalized cut. However unlike spectral approaches, the proposed algorithm scales to graphs with millions of nodes and edges. The algorithm consists of...

Segmentation: Clustering, Graph Cut and EM

Statistical Significance Based Graph Cut Regularization for Medical Image Segmentation

Graph cut minimization formulates the image segmentation as a linear combination of problem constraints. The salient constraints of the computer vision problems are data and smoothness which are combined through a regularization parameter. The main task of the regularization parameter is to determine the weight of the smoothness constraint on the graph energy. However, the difference in functio...