scholarly journals Image Segmentation using Euler Graphs

2010 ◽  
Vol 5 (3) ◽  
pp. 314 ◽  
Author(s):  
T.N. Janakiraman ◽  
P.V.S.S.R. Chandra Mouli
Keyword(s):  
Image Segmentation ◽  
Graph Theory ◽  
Polynomial Time ◽  
Graph Partitioning ◽  
Finite Graph ◽  
Objective Evaluation ◽  
Graph Partitioning Problem ◽  
Partitioning Problem ◽  
As Graph ◽  
Segmentation Problem

This paper presents a new algorithm for image segmentation problem using the concepts of Euler graphs in graph theory. By treating image as an undirected weighted non-planar finite graph (G), image segmentation is handled as graph partitioning problem. The proposed method locates region boundaries or clusters and runs in polynomial time. Subjective comparison and objective evaluation shows the efficacy of the proposed approach in different image domains.

A GRAPH BASED DAVIDSON ALGORITHM FOR THE GRAPH PARTITIONING PROBLEM

1999 ◽  
Vol 10 (02) ◽  
pp. 225-246 ◽  
Author(s):  
MICHAEL HOLZRICHTER ◽  
SUELY OLIVEIRA
Keyword(s):  
Graph Theory ◽  
Load Balancing ◽  
Graph Partitioning ◽  
Graph Laplacian ◽  
Matrix Factorizations ◽  
Graph Partitioning Problem ◽  
Spectral Graph ◽  
Partitioning Problem ◽  
Davidson Algorithm ◽  
Spectral Graph Partitioning

The problem of partitioning a graph such that the number of edges incident to vertices in different partitions is minimized, arises in many contexts. Some examples include its recursive application for minimizing fill-in in matrix factorizations and load-balancing for parallel algorithms. Spectral graph partitioning algorithms partition a graph using the eigenvector associated with the second smallest eigenvalue of a matrix called the graph Laplacian. The focus of this paper is the use graph theory to compute this eigenvector more quickly.

Graph Based Segmentation of Digital Images

2013 ◽  
pp. 182-199
Author(s):  
B.K. Tripathy ◽  
P.V.S.S.R. Chandra Mouli
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Object Detection ◽  
Image Classification ◽  
Digital Images ◽  
Graph Theoretic ◽  
Graph Partitioning Problem ◽  
Segmentation Algorithms ◽  
Partitioning Problem ◽  
Segmentation Problem

Image Segmentation is the process of dividing an image into semantically relevant regions. The problem is still an active area due to wide applications in object detection and recognition, image retrieval, image classification, et cetera. The problem is challenging due to its subjective nature. Many researchers addressed this problem by exploring graph theoretic principles. The key idea is the transformation of segmentation problem into graph partitioning problem by representing the image as a graph. The aim of this chapter is to study various graph based segmentation algorithms.

Learning Algorithm for the Uniform Graph Partitioning Problem

1998 ◽  
Vol 09 (02) ◽  
pp. 331-339 ◽  
Author(s):  
C. B. Chua ◽  
Kan Chen
Keyword(s):  
Graph Partitioning ◽  
Learning Algorithm ◽  
Graph Partitioning Problem ◽  
Simple Implementation ◽  
Partitioning Problem

We study the uniform graph partitioning problem using the learning algorithm proposed by one of us. We discuss the characteristics of the learning algorithm and compare the performance of the algorithm empirically with the Kernighan–Lin algorithm on a range of instances. Even with a simple implementation, the learning algorithm is capable of producing very good results.

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