Automatic image pixel clustering with an improved differential evolution

This article proposes an evolutionary-fuzzy clustering algorithm for automatically grouping the pixels of an image into different homogeneous regions. The algorithm does not require a prior knowledge of the number of clusters. The fuzzy clustering task in the intensity space of an image is formulated as an optimization problem. An improved variant of the differential evolution (DE) algorithm has been used to determine the number of naturally occurring clusters in the image as well as to refine the cluster centers. We report extensive performance comparison among the new method, a recently developed genetic-fuzzy clustering technique and the classical fuzzy c-means algorithm over a test suite comprising ordinary grayscale images and remote sensing satellite images. Such comparisons reveal, in a statistically meaningful way, the superiority of the proposed technique in terms of speed, accuracy and robustness.

[1]  Chien-Hsing Chou,et al.  A New Cluster Validity Measure for Clusters with Different Densities , 2004 .

[2]  Chien-Hsing Chou,et al.  A Competitive Learning Algorithm Using Symmetry , 1999 .

[3]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[4]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[5]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[6]  Amit Konar,et al.  Two improved differential evolution schemes for faster global search , 2005, GECCO '05.

[7]  Amit Konar,et al.  Computational Intelligence: Principles, Techniques and Applications , 2005 .

[8]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[9]  James C. Bezdek,et al.  Partially supervised clustering for image segmentation , 1996, Pattern Recognit..

[10]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[11]  Andries P. Engelbrecht,et al.  Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification , 2007 .

[12]  Ujjwal Maulik,et al.  A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification , 2005, Fuzzy Sets Syst..

[13]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Manish Sarkar,et al.  A clustering algorithm using an evolutionary programming-based approach , 1997, Pattern Recognit. Lett..

[15]  M.C. Clark,et al.  MRI segmentation using fuzzy clustering techniques , 1994, IEEE Engineering in Medicine and Biology Magazine.

[16]  Hichem Frigui,et al.  A Robust Competitive Clustering Algorithm With Applications in Computer Vision , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Witold Pedrycz,et al.  Fuzzy clustering with partial supervision , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[18]  James C. Bezdek,et al.  Clustering with a genetically optimized approach , 1999, IEEE Trans. Evol. Comput..

[19]  Christophe Rosenberger,et al.  Unsupervised clustering method with optimal estimation of the number of clusters: application to image segmentation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[20]  Andries Petrus Engelbrecht,et al.  Differential evolution methods for unsupervised image classification , 2005, 2005 IEEE Congress on Evolutionary Computation.

[21]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[22]  Ujjwal Maulik,et al.  Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[24]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[25]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[27]  A. K. Jain,et al.  Data Clustering : A , 2007 .

[28]  Bernard D. Flury,et al.  Why Multivariate Statistics , 1997 .

[29]  Mohan M. Trivedi,et al.  Low-Level Segmentation of Aerial Images with Fuzzy Clustering , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[31]  Yadong Wang,et al.  Improving fuzzy c-means clustering based on feature-weight learning , 2004, Pattern Recognit. Lett..

[32]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[33]  Ujjwal Maulik,et al.  Genetic clustering for automatic evolution of clusters and application to image classification , 2002, Pattern Recognit..

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

[35]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[36]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[37]  Yee Leung,et al.  Clustering by Scale-Space Filtering , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  G H Ball,et al.  A clustering technique for summarizing multivariate data. , 1967, Behavioral science.

[39]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization method for image clustering , 2005, Int. J. Pattern Recognit. Artif. Intell..

[40]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[41]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[42]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[43]  Chien-Hsing Chou,et al.  Short Papers , 2001 .