An Improved Image Segmentation Method based on Shannon Entropy and Biogeography based Optimization
Available Online February 2017.
- https://doi.org/10.2991/emcm-16.2017.105How to use a DOI?
- Biogeography based optimization; Evolutionary algorithms; Multilevel thresholding; Image segmentation; Shannon entropy
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/emcm-16.2017.105 ID - Feng2017/02 ER -