Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation

Abstract Although the interval type-2 fuzzy c-means clustering algorithm (IT2FCM) can well represent the uncertainty in data, there remain some problems to be solved: how to initialize cluster centers and how to determine fuzzifiers. In order to solve these issues of IT2FCM for color image segmentation, a pareto-based interval type-2 fuzzy c-means with multi-scale just noticeable difference color histogram (PIT2FC-MJND) is proposed in this paper. A multi-scale just noticeable difference (JND) color histogram is firstly constructed by using many distance thresholds and utilized to provide initial cluster centers. Then, a modified type-reduction and de-fuzzification mechanism on this multi-scale JND color histogram is designed for updating membership functions and cluster centers. Moreover, a pareto-based strategy for determining the combination of fuzzifiers is presented by using a global fuzzy compactness function and a fuzzy separation function which are based on the constructed multi-scale JND color histogram. The experimental results on real, Berkeley and Weizmann Images confirm the validity of the proposed approach.

[1]  Oscar C. Au,et al.  An adaptive unsupervised approach toward pixel clustering and color image segmentation , 2010, Pattern Recognit..

[2]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[3]  Oscar Castillo,et al.  An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms , 2012, Expert Syst. Appl..

[4]  Michael E. Farmer,et al.  Application of Genetic Algorithms for Wrapper-based Image Segmentation and Classification , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[5]  Santiago Aja-Fernández,et al.  A local fuzzy thresholding methodology for multiregion image segmentation , 2015, Knowl. Based Syst..

[6]  Oscar Castillo,et al.  Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems , 2015, Expert Syst. Appl..

[7]  Frank Y. Shih,et al.  Retinal vessels segmentation based on level set and region growing , 2014, Pattern Recognit..

[8]  Gonzalo Pajares,et al.  Improving segmentation velocity using an evolutionary method , 2015, Expert Syst. Appl..

[9]  Iraj Mahdavi,et al.  Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study , 2016, Appl. Soft Comput..

[10]  Mohamed-Jalal Fadili,et al.  On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series , 2001, Medical Image Anal..

[11]  Yu Li,et al.  Mahalanobis distance based on fuzzy clustering algorithm for image segmentation , 2015, Digit. Signal Process..

[12]  Oscar Castillo,et al.  Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control , 2015, Inf. Sci..

[13]  Frank Chung-Hoon Rhee,et al.  Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means , 2007, IEEE Transactions on Fuzzy Systems.

[14]  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.

[15]  Oscar Castillo,et al.  A generalized type-2 fuzzy granular approach with applications to aerospace , 2016, Inf. Sci..

[16]  Zexuan Ji,et al.  Interval-valued possibilistic fuzzy C-means clustering algorithm , 2014, Fuzzy Sets Syst..

[17]  Oscar Castillo,et al.  A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition , 2014, Appl. Soft Comput..

[18]  Somporn Chuai-Aree.  FUZZY C-MEAN: A STATISTICAL FEATURE CLASSIFICATION OF TEXT AND IMAGE SEGMENTATION METHOD , 2001 .

[19]  Doheon Lee,et al.  A novel initialization scheme for the fuzzy c-means algorithm for color clustering , 2004, Pattern Recognit. Lett..

[20]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ronen Basri,et al.  Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[23]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[24]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..

[25]  Oscar Castillo,et al.  An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques , 2017, Adv. Fuzzy Syst..

[26]  P. K. Mishra,et al.  Understanding Color Models: A Review , 2012 .

[27]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[28]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[29]  F. Chung-Hoon Rhee.  Uncertain Fuzzy Clustering: Insights and Recommendations , 2007 .

[30]  Thomas A. Runkler,et al.  The Generalized C Index for Internal Fuzzy Cluster Validity , 2016, IEEE Transactions on Fuzzy Systems.

[31]  Kalyanmoy Deb,et al.  Multi-objective Optimization , 2014 .

[32]  Juan R. Castro,et al.  A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems , 2016, Inf. Sci..

[33]  Haiqiao Huang,et al.  A robust adaptive clustering analysis method for automatic identification of clusters , 2012, Pattern Recognit..