Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(742.22KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Color Image Segmentation Based On Principal Component Analysis With Application of Firefly Algorithm And Gaussian Mixture Model
Donatella Giuliani
Pages - 101 - 112     |    Revised - 30-11-2018     |    Published - 31-12-2018
Volume - 12   Issue - 4    |    Publication Date - December 2018  Table of Contents
MORE INFORMATION
KEYWORDS
Color Image Segmentation, Image Clustering, Firefly Algorithm, Gaussian Mixture Model.
ABSTRACT
In this paper we propose a segmentation method for multi-spectral images in the HSV space, based on the Principal Component Analysis to generate grayscale images. Then the Firefly Algorithm has been applied on the gray-level images in a histogram-based research of cluster centroids. The FA is a metaheuristic optimization algorithm, centered on the flashing behaviour of fireflies. The Firefly Algorithm is performed to determine automatically the number of clusters and to select the gray levels for partitioning pixels into homogeneous regions. Successively, these gray values are employed during the initialization step of a Gaussian Mixture Model for estimation of parameters, evaluated through the Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be regarded as the prior probabilities of each component. Applying the Bayes rule, the posterior probabilities have been estimated and their maxima are used to assign each pixel to the clusters, according to their gray values.
CITED BY (0)  
1 S. Di Zenzo, "A note on the gradient of a multi-image", Comput. Vision and Graph. Image Process., vol. 36, (1986).
2 W. Skarbek, A. Koschan, "Colour Image Segmentation: a Survey", Technical Report, Technical University Berlin, (1994).
3 D. Comaniciu and P. Meer, "Robust Analysis of Feature Spaces: Color Image Segmentation", IEEE Conference on Computer Vision and Pattern Recognition, (1997).
4 A. Jurio, M. Pagol, M. Galar, C. Lopez-Molina, D. Paternain, "A Comparative Study of Different Color Spaces in Clustering Based Image Sementation", Communications in Computer and Information Science, Ed. Springer-Verlag, (2010).
5 I. H. Osman, G. Laporte, "Metaheuristics: a bibliography", Annals of Operations Research, Vol. 63, N. 5, pp. 511-623, (1996).
6 C. Blum, A. Roli, Metaheuristics in combinatorial optimization: Overview and conceptual comparison, ACM Computing Surveys, Vol. 35, N. 3, pp. 268-308 (2003).
7 F. Rothlauf, "Design of Modern Heuristics Principles and Application", Springer (2011).
8 C. Deepika, J. Nithya "Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey", International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, Vol.8, N.10 (2014).
9 J. Senthilnath, S.M. Okmar, V. Mani, "Clustering using a Firefly Algorithm: Performance Study", Swarm and Evolutionary Computation, Vol.1, pp 164-171, (2011).
10 J. Senthilnath, S. Kulkarni, J.A. Benediktsson, X.S. Yang, "A Novel Approach for Multispectral Satellite Image Classi?cation Based on the Bat Algorithm", IEEE Geoscience and Remote Sensing Letters, (2016).
11 V. Rajinikantha, M. S. Couceiro,"RGB Histogram based Color Image Segmentation Using Firefly Algorithm", Procedia Computer Science, Ed Elsevier, Vol. 46, pp. 1449 -1457, (2015).
12 M.H. Horng, T.W. Jiang, "Multilevel Image Thresholding Selection Based on the Firefly Algorithm", Proc. IEEE 7th Intern. Conf. on Ubiquitous Intelligence and Computing (2010).
13 L. Lucchese, S. Mitra, "Color Image Segmentation: A State-of-the-Art Survey", Image Processing, Vision, and Pattern Recognition, Proc. of the Indian National Science Academy (INSA-A), Vol. 67, N. 2, pp. 207-221, (2001).
14 A.P. Vartak , V, Mankar, "Color Image Segmentation: a Survey", Intern. Journ. Emerging Techon. Advanced Engineering., Vol. 3, N. 2., (2013).
15 N.M. Zaitoun, J.A. Musbah, "Survey on Image Segmentation Techniques", Proc. Int. Conf. on Communication, Management and Information Technology, Ed. Elsevier, (2015).
16 S. Dikbas, T. Arici, Y. Altunbasak, "Chrominance Edge preserving Grayscale Transformation with approximate First Principal Component for Color Edge Detection", Proc. IEEE Conf. Image Process. (ICIP'07), Vol. 9, pp. 497-500, (2007).
17 X. S. Yang, Nature-inspired Metaheuristic Algorithms, Luniver Press, United Kingdom, (2008).
18 X. S. Yang, Firefly Algorithm, Stochastic Test Functions and Design Optimization, International Journal of Bio-Inspired Computation, Vol. 2, pp. 78-84, (2010).
19 X.S. Yang, "Firefly Algorithm, Levy Flights and Global Optimization Research and Development", Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer London, pp. 209-218, (2010).
20 J. Senthilnath, S. N. Omkar, V. Mani, "Clustering using Firefly Algorithm: Performance Study", Swarm and Evolutionary Computation, Elsevier, Vol.1, pp 164-171, (2011).
21 S. Fong, S. Deb, X.S. Yang, Y. Zhuang, "Towards Enhancement of Performance of K- Means Clustering Using Nature-Inspired Optimization Algorithms", The Scientific World Journal, Vol. 2014, (2014).
22 B.G. Lindsay, "Mixture Models: Theory, Geometry and Applications", NFS-CBMS Regional Conference Series in Probability and Statistics (1995).
23 G.J. McLachlan, K.E. Basford, Mixture Models: Inference and Applications to Clustering, Ed. Marcel Dekker, New York, (1988).
24 D. Giuliani, "A Grayscale Segmentation Approach using the Firefly Algorithm and the Gaussian Mixture Model", International Journal of Swarm Intelligence Research, Vol. 9, Issue 1, Ed. IGI Global (2017).
25 D. Martin, C. Fowlkes, D. Tal, J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," ICCV, (2001).
26 B.G. Lindsay, "Mixture Models: Theory, Geometry and Applications", NFS-CBMS Regional Conference Series in Probability and Statistics, (1995).
27 G.J. McLachlan, K.E. Basford, Mixture Models: Inference and Applications to Clustering, Ed. Marcel Dekker, New York, (1988).
28 K. Jaskirat, A. Sunil, V. Renu, "A comparative analysis of thresholding and edge detection segmentation techniques", International Journal of Computer Applications, Vol. 39, (2012).
29 R.N. Nihar, K.M. Bikram, K.R. Amiya, "A Time Efficient Clustering Algorithm for Gray Scale Image Segmentation", International Journal of Computer Vision and Image Processing, Vol. 3, N. 1, pp 22-32, (2013).
Dr. Donatella Giuliani
Scientific and Didactic Polo of Rimini, University of Bologna - Italy
giulianidonatella@libero.it