Comparison between BBO and Genetic Algorithm

Segmentation" refers to the process of dividing a digital image into multiple segments such as sets of pixels, also known as super pixels. The main objective of segmentation is to simplify and/or change the representation of an image into meaningful image that is more appropriate and easier to analyze. "Image segmentation" is an important aspect of digital image processing. Color images can increase the quality of segmentation, but increase the complexity of the problem. Evolutionary algorithms are well suited to optimizing complex problems such as image segmentation. In this paper two optimization algorithms are explored for image segmentation i.e Genetic algorithm and Biogeography based optimization algorithm. And then compare both these algorithm to show the better optimization and noise free color image segmentation of BBO algorithm as compared to GA. This paper also explores the limitations of GA over

[1]  Riccardo Poli,et al.  Genetic Programming with User-Driven Selection : Experiments on the Evolution of Algorithms for Image Enhancement , 1997 .

[2]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Joe Suzuki,et al.  A Markov chain analysis on simple genetic algorithms , 1995, IEEE Trans. Syst. Man Cybern..

[4]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[5]  William M. Wells,et al.  An EM algorithm for shape classification based on level sets , 2005, Medical Image Anal..

[6]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[7]  Neal R. Harvey,et al.  Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images , 2003, SPIE Medical Imaging.

[8]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[9]  Dan Simon,et al.  Biogeography-based optimization combined with evolutionary strategy and immigration refusal , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[11]  Dan Simon,et al.  A Probabilistic Analysis of a Simplified Biogeography-Based Optimization Algorithm , 2011, Evolutionary Computation.

[12]  Bernard F. Buxton,et al.  Evolving edge detectors with genetic programming , 1996 .

[13]  Fachbereich Informatik,et al.  Programmatic Compression of Images and Sound , 1996 .

[14]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[15]  Neal R. Harvey,et al.  GENIE: a hybrid genetic algorithm for feature classification in multispectral images , 2000, SPIE Optics + Photonics.