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Result Analysis of Image Segmentation using Hierarchical Merge Tree

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

Ankit Bihone and Imran Khan. Result Analysis of Image Segmentation using Hierarchical Merge Tree. International Journal of Computer Applications 173(3):20-22, September 2017. BibTeX

@article{10.5120/ijca2017915270,
	author = {Ankit Bihone and Imran Khan},
	title = {Result Analysis of Image Segmentation using Hierarchical Merge Tree},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {3},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {20-22},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume173/number3/28315-2017915270},
	doi = {10.5120/ijca2017915270},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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.

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Keywords

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