Multicomponent image segmentation: a comparative analysis between a hybrid genetic algorithm and self‐organizing maps

Image segmentation is an essential process in image analysis. Several methods have been developed to segment multicomponent images and the success of these methods depends on the characteristics of the acquired image and the percentage of imperfections in the process of its acquisition. Many of the segmentation methods are parametric, which means that many parameters need to be computed or provided before the segmentation process, and any method that works on one type of multicomponent image cannot necessarily work on another. In addition, many segmentation methods are supervised, where a priori knowledge is needed, such as the number of classes. To overcome these obstacles, a self‐organizing map (SOM), which is an unsupervised nonparametric method, was used to segment four different types of multicomponent images (Landsat, SPOT, IKONOS and CASI), and the results compared to those of a new nonparametric unsupervised genetic algorithm (GA) for image segmentation. To improve the performance of the GA, a hill‐climbing process and another random heuristic module were added to escape the local‐minima trap and to improve the speed of the GA; the new algorithm is called the hybrid genetic algorithm (HGA). Verification of the results was performed using two different techniques: field verification and the functional model. These verification techniques show that the HGA is more accurate in multicomponent image segmentation than the SOM.

[1]  Martin D. Levine,et al.  Vision in Man and Machine , 1985 .

[2]  Hyun Seung Yang,et al.  Robust image segmentation using genetic algorithm with a fuzzy measure , 1996, Pattern Recognit..

[3]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[4]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[5]  William K. Pratt,et al.  Digital image processing (2nd ed.) , 1991 .

[6]  Matti Pietikäinen,et al.  Unsupervised texture segmentation using feature distributions , 1997, Pattern Recognit..

[7]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[8]  Christophe Collet,et al.  Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery , 2000, Pattern Recognit..

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  David J. Evans,et al.  A parallel genetic algorithm for cell image segmentation , 2001, Electron. Notes Theor. Comput. Sci..

[11]  Hujun Yin,et al.  On the Distribution and Convergence of Feature Space in Self-Organizing Maps , 1995, Neural Computation.

[12]  Bir Bhanu,et al.  Adaptive image segmentation using a genetic algorithm , 1989, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[14]  Shu-Hung Leung,et al.  The genetic search approach. A new learning algorithm for adaptive IIR filtering , 1996, IEEE Signal Process. Mag..

[15]  Bugao Xu,et al.  AUTOMATIC COLOR IDENTIFICATION IN PRINTED FABRIC IMAGES BY A FUZZY-NEURAL NETWORK , 2002 .

[16]  H.J. Kim,et al.  A genetic algorithm-based segmentation of Markov random field modeled images , 2000, IEEE Signal Processing Letters.

[17]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[18]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[19]  Shunichiro Oe,et al.  Evolutionary segmentation of texture image using genetic algorithms towards automatic decision of optimum number of segmentation areas , 1999, Pattern Recognit..

[20]  Giosuè Lo Bosco.  A Genetic Algorithm for Image Segmentation , 2001, ICIAP.

[21]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[22]  K. Paithoonwattanakij,et al.  Image segmentation by fuzzy rule and Kohonen-constraint satisfaction fuzzy C-mean , 1997, Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat..

[23]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[24]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[25]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[26]  Kuldeep Kumar,et al.  Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[27]  Claude L. Fennema,et al.  Scene Analysis Using Regions , 1970, Artif. Intell..

[28]  Keith Price,et al.  Picture Segmentation Using a Recursive Region Splitting Method , 1998 .

[29]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[30]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[31]  Ying Xu,et al.  A segmentation algorithm for noisy images: Design and evaluation , 1998, Pattern Recognit. Lett..

[32]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[33]  Hugues Benoit-Cattin,et al.  Image segmentation functional model , 2004, Pattern Recognit..

[34]  J. Amini,et al.  Generalized Cooccurrence Matrix to Classify IRS-1D Images using Neural Network , 2004 .

[35]  Bugao Xu,et al.  Automatic color identification in printed fabrics by a neural-fuzzy system , 2002 .

[36]  Hang Joon Kim,et al.  MRF model based image segmentation using hierarchical distributed genetic algorithm , 1998 .

[37]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .