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Image Segmentation using MSNCut Algorithm

by Basavaprasad B.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 3
Year of Publication: 2017
Authors: Basavaprasad B.
10.5120/ijca2017913313

Basavaprasad B. . Image Segmentation using MSNCut Algorithm. International Journal of Computer Applications. 162, 3 ( Mar 2017), 27-30. DOI=10.5120/ijca2017913313

@article{ 10.5120/ijca2017913313,
author = { Basavaprasad B. },
title = { Image Segmentation using MSNCut Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 3 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number3/27223-2017913313/ },
doi = { 10.5120/ijca2017913313 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:59.542469+05:30
%A Basavaprasad B.
%T Image Segmentation using MSNCut Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 3
%P 27-30
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a hybrid technique for the segmentation of image has been proposed and it is named as MSNCut technique. Here image segmentation is the process of dividing the given image into number of regions that possess similar properties such as color, texture and intensity which are useful for the image analysis. The image is generic here, in other words image may be tree, river, building, medical or any general image. In this proposed method first the input image is pre-processed by mean shift algorithm to divide it into its constituent regions. Then the resultant image is represented as a bi-partite graph and finally the resultant graph (image) is processed under normalized cut to classify the image into meaningful classes.

References
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  2. Nida M. Zaitoun, Musbah J and Aqel, "Survey on Image Segmentation Techniques", International Conference on Communication, Management and Information Technology (ICCMIT), 2015.
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  4. Cheng and Yizong, “Mean Shift, Mode Seeking and Clustering””, An IEEE Transaction on Pattern Analysis and Machine Intelligence, Volume: 17, Issue:8, pp:790-799, 1995.
  5. Basavaprasad B and Ravi M, "Enhanced Color Image Segmentation by Graph Cut Method in General and Medical Image", Lecture Notes in Bioengineering, Advancements of Medical Electronics, Proceedings of the First International Conference, ICAME, 2015.
  6. Shi J and Malik J, “Image Segmentation and the Normalized Cuts", Machine Intelligence and Pattern Analysis, an IEEE Transactions, Volume 22, 2000.
  7. Basavaprasad B, Ravindra S Hegadi, "Color Image Segmentation Using Adaptive GrowCut Method", International Conference on Advanced Computing Technologies and Applications (ICACTA), 2015
  8. Fulkerson D and Ford L, “Flow in networks”, Princeton University Press, 1962.
  9. Koschan Aand Skarbek W, "A Survey on Color Image Segmentation", Technical Report, Tech. University of Berlin, 1994.
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation MSNCut Mean Shift Minimal cut Clustering.

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