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Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm

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IJCA Proceedings on International Conference on Communication, Computing and Information Technology
© 2015 by IJCA Journal
ICCCMIT 2014 - Number 3
Year of Publication: 2015
Authors:
B. Joyce Preethi
R. Angel Sujitha
V. Rajinikanth

Joyce B Preethi, Angel R Sujitha and V Rajinikanth. Article: Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm. IJCA Proceedings on International Conference on Communication, Computing and Information Technology ICCCMIT 2014(3):10-16, March 2015. Full text available. BibTeX

@article{key:article,
	author = {B. Joyce Preethi and R. Angel Sujitha and V. Rajinikanth},
	title = {Article: Otsu based Multi-level Image Segmentation using Brownian Bat Algorithm},
	journal = {IJCA Proceedings on International Conference on Communication, Computing and Information Technology},
	year = {2015},
	volume = {ICCCMIT 2014},
	number = {3},
	pages = {10-16},
	month = {March},
	note = {Full text available}
}

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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