A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering

In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over segmentation could be avoided. Indeed, the bilateral filtering, as a preprocessing step, eliminates the unnecessary details of the image and results in a few numbers of pixons, faster performance and more robustness against unwanted environmental noises. Then, the obtained pixonal image is segmented using the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.

[1]  R. C. Puetter,et al.  Pixon‐based multiresolution image reconstruction and the quantification of picture information content , 1995, Int. J. Imaging Syst. Technol..

[2]  Sang Uk Lee,et al.  Integrated Position Estimation Using Aerial Image Sequences , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[5]  Pierre Kornprobst,et al.  Bilateral Filtering , 2009 .

[6]  Zoltan Kato,et al.  A Markov random field image segmentation model for color textured images , 2006, Image Vis. Comput..

[7]  Tianzi Jiang,et al.  Pixon-based image segmentation with Markov random fields , 2003, IEEE Trans. Image Process..

[8]  Dorit S. Hochbaum,et al.  An efficient algorithm for image segmentation, Markov random fields and related problems , 2001, JACM.

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

[10]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  Francisco de A. T. de Carvalho,et al.  Fuzzy c-means clustering methods for symbolic interval data , 2007, Pattern Recognit. Lett..

[12]  David A. Clausi,et al.  K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation , 2002, Pattern Recognit..

[13]  Frank Y. Shih,et al.  Automatic seeded region growing for color image segmentation , 2005, Image Vis. Comput..

[14]  Robert K. Pina,et al.  BAYESIAN IMAGE RECONSTRUCTION: THE PIXON AND OPTIMAL IMAGE MODELING , 1993 .

[15]  Georgios P. Papamichail,et al.  The k-means range algorithm for personalized data clustering in e-commerce , 2007, Eur. J. Oper. Res..

[16]  Michel Herbin,et al.  A 'no-threshold' histogram-based image segmentation method , 2002, Pattern Recognit..

[17]  Josiane Zerubia,et al.  Unsupervised parallel image classification using Markovian models , 1999, Pattern Recognit..

[18]  Tianzi Jiang,et al.  A novel pixon-representation for image segmentation based on Markov random field , 2008, Image Vis. Comput..