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Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm

Published on March 2015 by B. Joyce Preethi, R. Angel Sujitha, V. Rajinikanth
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International Conference on Communication, Computing and Information Technology
Foundation of Computer Science USA
ICCCMIT2014 - Number 3
March 2015
Authors: B. Joyce Preethi, R. Angel Sujitha, V. Rajinikanth
aaf97d40-b442-43d6-a603-02937cc871ae

B. Joyce Preethi, R. Angel Sujitha, V. Rajinikanth . Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm. International Conference on Communication, Computing and Information Technology. ICCCMIT2014, 3 (March 2015), 10-16.

@article{
author = { B. Joyce Preethi, R. Angel Sujitha, V. Rajinikanth },
title = { Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm },
journal = { International Conference on Communication, Computing and Information Technology },
issue_date = { March 2015 },
volume = { ICCCMIT2014 },
number = { 3 },
month = { March },
year = { 2015 },
issn = 0975-8887,
pages = { 10-16 },
numpages = 7,
url = { /proceedings/icccmit2014/number3/19781-7027/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication, Computing and Information Technology
%A B. Joyce Preethi
%A R. Angel Sujitha
%A V. Rajinikanth
%T Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm
%J International Conference on Communication, Computing and Information Technology
%@ 0975-8887
%V ICCCMIT2014
%N 3
%P 10-16
%D 2015
%I International Journal of Computer Applications
Abstract

In this work, bi-level and multi-level segmentation is proposed for the grey image dataset using a novel Brownian Bat Algorithm (BBA). Maximization of Otsu's between-class variance function is chosen as the objective function. The performance of the proposed CBA is demonstrated by considering five benchmark images and compared with the existing bat algorithms such as Traditional Bat Algorithm (TBA) and the Lévy flight Bat Algorithm (LBA). The performance appraisal between the proposed and existing bat algorithms are done using existing constraints such as objective function, Root Mean Squared Error (RMSE), Peak to Signal Ratio (PSNR), Structural Dissimilarity (DSSIM) index, and algorithm convergence. The result evident that proposed CBA offers better values for objective function, RMSE, PSNR and DSSIM, whereas TBA and LBA offers faster convergence compared to BBA.

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Index Terms

Computer Science
Information Sciences

Keywords

Histogram Otsu Bat Algorithm Milti-level Thresholding