Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D

Graphical abstractDisplay Omitted HighlightsThe proposed algorithm can preserve image details while removing noise for image segmentation.Two problem-specific techniques are introduced to achieve well performance for image segmentation.OBL is used in multi-objective optimization to achieve optimal solutions with a better convergence speed. In order to achieve robust performance of preserving significant image details while removing noise for image segmentation, this paper presents a multi-objective evolutionary fuzzy clustering (MOEFC) algorithm to convert fuzzy clustering problems for image segmentation into multi-objective problems. The multi-objective problems are optimized by multi-objective evolutionary algorithm with decomposition. The decomposition strategy is adopted to project the multi-objective problem into a number of sub-problems. Each sub-problem represents a fuzzy clustering problem incorporating local information for image segmentation. Opposition-based learning is utilized to improve search capability of the proposed algorithm. Two problem-specific techniques, an adaptive weighted fuzzy factor and a mixed population initialization, are introduced to improve the performance of the algorithm. Experiment results on synthetic and real images illustrate that the proposed algorithm can achieve a trade-off between preserving image details and removing noise for image segmentation.

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

[2]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[3]  Xiaowei Yang,et al.  A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises , 2011, IEEE Transactions on Fuzzy Systems.

[4]  Vincenzo Catania,et al.  An evolutionary fuzzy c-means approach for clustering of bio-informatics databases , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[5]  Shahryar Rahnamayan,et al.  A novel population initialization method for accelerating evolutionary algorithms , 2007, Comput. Math. Appl..

[6]  Paul L. Rosin,et al.  Evaluation of global image thresholding for change detection , 2003, Pattern Recognit. Lett..

[7]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[8]  Ujjwal Maulik,et al.  An Interactive Approach to Multiobjective Clustering of Gene Expression Patterns , 2013, IEEE Transactions on Biomedical Engineering.

[9]  Maoguo Gong,et al.  Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation , 2013, IEEE Transactions on Image Processing.

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

[11]  M. Stella Atkins,et al.  Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI , 1996, IEEE Trans. Medical Imaging.

[12]  Hong Yan,et al.  An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation , 2003, IEEE Transactions on Medical Imaging.

[13]  Fang Liu,et al.  A modified objective function method with feasible-guiding strategy to solve constrained multi-objective optimization problems , 2014, Appl. Soft Comput..

[14]  László Szilágyi,et al.  Lessons to learn from a mistaken optimization , 2014, Pattern Recognit. Lett..

[15]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[16]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[17]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[18]  Ujjwal Maulik,et al.  Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[20]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  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.

[23]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[24]  Stelios Krinidis,et al.  A Robust Fuzzy Local Information C-Means Clustering Algorithm , 2010, IEEE Transactions on Image Processing.

[25]  María José del Jesús,et al.  Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges , 2015, Knowl. Based Syst..

[26]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[27]  Martial Hebert,et al.  Measures of Similarity , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[28]  Jun Wang,et al.  Cooperative Coevolution for Large-Scale Optimization Based on Kernel Fuzzy Clustering and Variable Trust Region Methods , 2014, IEEE Transactions on Fuzzy Systems.

[29]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[30]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[31]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Marina Meila,et al.  Comparing clusterings: an axiomatic view , 2005, ICML.

[33]  S.M. Szilagyi,et al.  MR brain image segmentation using an enhanced fuzzy C-means algorithm , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[34]  Maoguo Gong,et al.  Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering , 2012, IEEE Transactions on Image Processing.

[35]  Qingfu Zhang,et al.  A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[36]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[37]  Witold Pedrycz,et al.  Advances in Fuzzy Clustering and its Applications , 2007 .

[38]  Xin Yao,et al.  An Evolutionary Multiobjective Approach to Sparse Reconstruction , 2014, IEEE Transactions on Evolutionary Computation.

[39]  Hwee Kuan Lee,et al.  Comments on “A Robust Fuzzy Local Information C-Means Clustering Algorithm” , 2013, IEEE Transactions on Image Processing.

[40]  Rajkumar Roy,et al.  Multi-objective Optimisation Of Rolling Rod Product Design Using Meta-modelling Approach , 2002, GECCO.

[41]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[42]  Qingfu Zhang,et al.  MOEA/D-ACO: A Multiobjective Evolutionary Algorithm Using Decomposition and AntColony , 2013, IEEE Transactions on Cybernetics.

[43]  Hui Zhang,et al.  An entropy-based objective evaluation method for image segmentation , 2003, IS&T/SPIE Electronic Imaging.

[44]  Maoguo Gong,et al.  Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection , 2008, Evolutionary Computation.

[45]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[46]  Pengfei Shi,et al.  An improved ant colony algorithm for fuzzy clustering in image segmentation , 2007, Neurocomputing.

[47]  Kalyanmoy Deb,et al.  Finding Knees in Multi-objective Optimization , 2004, PPSN.

[48]  Mario Ventresca,et al.  Improving the Convergence of Backpropagation by Opposite Transfer Functions , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[49]  Ujjwal Maulik,et al.  Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[51]  Marian B. Gorzalczany,et al.  A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability , 2016, Appl. Soft Comput..

[52]  Francisco Herrera,et al.  A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions , 2013, IEEE Transactions on Fuzzy Systems.

[53]  Rajkumar Roy,et al.  Evolutionary computing in manufacturing industry: an overview of recent applications , 2005, Appl. Soft Comput..

[54]  Vincenzo Catania,et al.  Psychology with soft computing: An integrated approach and its applications , 2008, Appl. Soft Comput..

[55]  Xiao Zhang,et al.  A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving , 2015, Inf. Sci..

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

[57]  Jing Liu,et al.  An organizational coevolutionary algorithm for classification , 2006, IEEE Trans. Evol. Comput..

[58]  Hanqiang Liu,et al.  A multiobjective spatial fuzzy clustering algorithm for image segmentation , 2015, Appl. Soft Comput..

[59]  Swagatam Das,et al.  Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm , 2010, Inf. Sci..