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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
  1. Ting Liu, Mojtaba Seyedhosseini, and Tolga Tasdizen “Image Segmentation Using Hierarchical Merge Tree” IEEE transactions on image processing, vol. 25, no. 10, october 2016
  2. John M. Gauch “Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies” IEEE transactions on image processing, vol. 8, no. 1, January 1999
  3. Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras Aggelos K. Katsaggelos “Hybrid Image Segmentation Using Watersheds and Fast Region Merging” IEEE transactions on image processing, vol. 7, no. 12, december 1998
  4. Jianbo Shi, Jitendra Malik “Normalized Cuts and Image Segmentation” IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, Issue 8, August 2000, pages 888-905.
  5. Chiou-Shann Fuh, Shun-Wen Cho, and Kai Essig “Hierarchical Color Image Region Segmentation for Content-Based Image Retrieval System” IEEE transactions on image processing, vol. 9, no. 1, January 2000
  6. M. Seyedhosseini and T. Tasdizen, “Semantic image segmentation with contextual hierarchical models,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 38, no. 5, pp. 951–964, May 2015.
  7. S. Belongie, C. Carson, H. Greenspan, and J. Malik, “Color- and texturebased image segmentation using EM and its application to contentbased image retrieval,” in Proc. Int. Conf. Comput. Vis., Jan. 1998, pp. 675–682.
  8. D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603–619, May 2002.
  9. O.J. Morris, M.de J.Lee, and A.G. Constantinides, 1986. A unified method for segmentation and edge detection using graph theory. Proc. ICASSP, pp. 2051-2054.
  10. A. Vedaldi and S. Soatto, “Quick shift and kernel methods for mode seeking,” in Proc. Eur. Conf. Comput. Vis., 2008, pp. 705–718.
  11. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888–905, Aug. 2000.
  12. P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-based image segmentation,” Int. J. Comput. Vis., vol. 59, no. 2, pp. 167–181, Sep. 2004.
  13. W. Skarbek, A. Koschan, Colour Image Segmentation { A Survey, Technical Report 94-32, Technical University Berlin, October 1994.
  14. H. Zhu, F. Meng, J. Cai, and S. Lu, “Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation,” J. Vis. Commun. Image Represent., vol. 34, pp. 12–27, Jan. 2016.
  15. Dipti Patra. Brain MR image segmentation using Markov random field model and Tabu search strategy. PhD thesis, National Institute of Technology, Rourkela, India, Department of Electrical Engineering, 2005.
  16. D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. 8th IEEE Int. Conf. Comput. Vis. (ICCV), vol. 2. Jul. 2001, pp. 416–423.
  17. J. Shotton, J. Winn, C. Rother, and A. Criminisi, “TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation,” in Proc. Eur. Conf. Comput. Vis., 2006, pp. 1–15.
  18. S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” in Proc. Int. Conf. Comput. Vis., Sep./Oct. 2009, pp. 1–8.
  19. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. ZissermanThe PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. acessed on Dec. 9, 2014. [Online]. Available:http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html
  20. N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from RGBD images,” in Proc. Eur. Conf. Comput. Vis., 2012, pp. 746–760.
  21. K. S. Fu and J. K. Mui, A survey on image segmentation, Pattern Recognir. 13, 1981, 3-16.
  22. J. Besag. On the statistical analysis of dirty pictures. Journal of Royal Statistical Society B, 62:259–302, 1986.
Index Terms

Computer Science
Information Sciences

Keywords

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

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