An Improved Image Segmentation Method based on Shannon Entropy and Biogeography based Optimization

DOI

10.2991/emcm-16.2017.105How to use a DOI?

Keywords

Biogeography based optimization; Evolutionary algorithms; Multilevel thresholding; Image segmentation; Shannon entropy

Abstract

For the purpose of improve the effect of multilevel thresholding image segmentation, a new evolutionary optimization algorithm based on the science of biogeography for global optimization has been bring in namely Biogeography based optimization (BBO). In this paper we propose an improvement to BBO. In order to improve the diversity of population and to enhance its exploration ability, the Gaussian mutation operator is integrated into biogeography based optimization (BBO). And we combine this improved evolutionary algorithm and Shannon entropy to get multilevel thresholds of image segmentation. Experiments have been conducted on several images and compared with other algorithm namely ABC and DE.Simulation results and comparisons demonstrate the proposed BBO algorithm is better in terms of the quality of the solutions obtained.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mengqing Feng
PY  - 2017/02
DA  - 2017/02
TI  - An Improved Image Segmentation Method based on Shannon Entropy and Biogeography based Optimization
BT  - Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcm-16.2017.105
DO  - 10.2991/emcm-16.2017.105
ID  - Feng2017/02
ER  -