An improved ant colony algorithm for fuzzy clustering in image segmentation

Ant colony algorithm (ACA), inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. In this paper, it is used for fuzzy clustering in image segmentation. Three features such as gray value, gradient and neighborhood of the pixels, are extracted for the searching and clustering process. Unexpectedly, tests show that it is time consuming when dealing with the vast image data. In view of this drawback, improvements have been made by initializing the clustering centers and enhancing the heuristic function to accelerate the searching process. Experiments and comparisons are done to show that the improved ACA-based image segmentation is an efficient and effective approach.

[1]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[2]  Sudeep Sarkar,et al.  Comparison of Edge Detectors: A Methodology and Initial Study , 1998, Comput. Vis. Image Underst..

[3]  Minglun Gong,et al.  Quadtree-based genetic algorithm and its applications to computer vision , 2004, Pattern Recognit..

[4]  Lawrence O. Hall,et al.  Fast Accurate Fuzzy Clustering through Data Reduction , 2003 .

[5]  Jae Won Lee,et al.  Content-based image classification using a neural network , 2004, Pattern Recognit. Lett..

[6]  Amir Averbuch,et al.  Color image segmentation based on adaptive local thresholds , 2005, Image Vis. Comput..

[7]  Michel Jourlin,et al.  A new minimum variance region growing algorithm for image segmentation , 1997, Pattern Recognit. Lett..

[8]  Hong Zheng,et al.  Automatic inspection of metallic surface defects using genetic algorithms , 2002 .

[9]  Jun Shen,et al.  Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement , 2003, Pattern Recognit..

[10]  Qu Ying-Dong,et al.  A fast subpixel edge detection method using Sobel-Zernike moments operator , 2005, Image Vis. Comput..

[11]  Nauman Zaffar,et al.  Object segmentation using feature based conditional morphology , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[12]  Hanqing Lu,et al.  Texture representation based on pattern map , 2004, Signal Process..

[13]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..

[14]  Lily R. Liang,et al.  Competitive fuzzy edge detection , 2003, Appl. Soft Comput..

[15]  Han Wang,et al.  Real-time corner detection algorithm for motion estimation , 1995, Image Vis. Comput..

[16]  Sudeep Sarkar,et al.  Comparison of edge detectors: a methodology and initial study , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Hua Liu.  Restoration of distorted digital images and similarity measure between images By Student : , 1999 .

[18]  L. Joshua Leon,et al.  Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..

[19]  Daniele D. Giusto,et al.  FACE: fast active-contour curvature-based evolution , 2004, Signal Process. Image Commun..

[20]  Andreas Hoppe,et al.  Robust and automated unimodal histogram thresholding and potential applications , 2004, Pattern Recognit..

[21]  Hsueh-I Lu,et al.  Efficient region segmentation on compressed gray images using quadtree and shading representation , 2004, Pattern Recognit..