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A Survey on Traditional and Graph Theoretical Techniques for Image Segmentation

Published on February 2014 by Basavaprasad B., Ravindra S. Hegadi
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National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 1
February 2014
Authors: Basavaprasad B., Ravindra S. Hegadi
1722692d-1a4d-4465-bfee-202b0936a028

Basavaprasad B., Ravindra S. Hegadi . A Survey on Traditional and Graph Theoretical Techniques for Image Segmentation. National Conference on Recent Advances in Information Technology. NCRAIT, 1 (February 2014), 38-46.

@article{
author = { Basavaprasad B., Ravindra S. Hegadi },
title = { A Survey on Traditional and Graph Theoretical Techniques for Image Segmentation },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 1 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 38-46 },
numpages = 9,
url = { /proceedings/ncrait/number1/15143-1408/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A Basavaprasad B.
%A Ravindra S. Hegadi
%T A Survey on Traditional and Graph Theoretical Techniques for Image Segmentation
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 1
%P 38-46
%D 2014
%I International Journal of Computer Applications
Abstract

Image segmentation is the process of subdividing a digital image into its systematized regions or objects which is useful in image analysis. In this review paper, we carried out an organized survey of many image segmentation techniques which are flexible, cost effective and computationally more efficient. We classify these segmentation methods into three categories: the traditional methods, graph theoretical methods and combination of both traditional and graph theoretical methods. In the second and third category of image segmentation approaches, the image is modeled as a weighted and undirected graph. Normally a pixel or a group of pixels are connected with nodes. The edge weights represent the dissimilarity between the neighborhood pixels. The graph or the image is then divided according to a benchmark designed to model good clusters. Every partition of the nodes or the pixels as output from these algorithms is measured as an object segment in an image representing a graph. Some of the popular algorithms are thresholding, normalized cuts, iterated graph cut, clustering method, watershed transformation, minimum cut, grey graph cut, and minimum spanning tree-based segmentation.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Histogram Neural Network Thresholding Watershed Transformation Clustering Quadtree Graph Theoretical Methods Euler Graph Minimal Spanning Tree Grey Graph Cut Grabcut.