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Result Analysis of Image Segmentation using Hierarchical Merge Tree

by Ankit Bihone, Imran Khan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 3
Year of Publication: 2017
Authors: Ankit Bihone, Imran Khan
10.5120/ijca2017915270

Ankit Bihone, Imran Khan . Result Analysis of Image Segmentation using Hierarchical Merge Tree. International Journal of Computer Applications. 173, 3 ( Sep 2017), 20-22. DOI=10.5120/ijca2017915270

@article{ 10.5120/ijca2017915270,
author = { Ankit Bihone, Imran Khan },
title = { Result Analysis of Image Segmentation using Hierarchical Merge Tree },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 3 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 20-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number3/28315-2017915270/ },
doi = { 10.5120/ijca2017915270 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:16.265818+05:30
%A Ankit Bihone
%A Imran Khan
%T Result Analysis of Image Segmentation using Hierarchical Merge Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 3
%P 20-22
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper aims to advance research in image segmentation by developing robust techniques for evaluating image segmentation algorithms. The key contributions of this work are as follows. First, we investigate the characteristics of existing measures for supervised evaluation of automatic image segmentation algorithms. We show which of these measures is most effective at distinguishing perceptually accurate image segmentation from inaccurate segmentation. Second, we develop a complete framework for evaluating interactive segmentation algorithms by means of user experiments. We explore four strategies for this simulation, and demonstrate that the best of these produces results very similar to those from the user experiments.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Clustering Region-Based RSST - Recursive Shortest-Spanning Tree.

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