Image segmentation with a fuzzy clustering algorithm based on Ant-Tree

Authors:
Xiaochun Yang;Weidong Zhao;Yufei Chen;Xin Fang
Affiliations:
Research Center of CAD, Tongji University, Shanghai 200092, PR China;Research Center of CAD, Tongji University, Shanghai 200092, PR China;Research Center of CAD, Tongji University, Shanghai 200092, PR China;Research Center of CAD, Tongji University, Shanghai 200092, PR China
Venue:
Signal Processing
Year:
2008

Citing 10
Cited 5

A new minimum variance region growing algorithm for image segmentation

Pattern Recognition Letters
Comparison of edge detectors: a methodology and initial study

Computer Vision and Image Understanding
Quantitative evaluation of color image segmentation results

Pattern Recognition Letters
Multiresolution Color Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation using fuzzy divergence

Pattern Recognition Letters
Texture representation based on pattern map

Signal Processing
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation

Pattern Recognition
A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

Pattern Recognition
Image segmentation by clustering of spatial patterns

Pattern Recognition Letters
Image segmentation by histogram thresholding using fuzzy sets

IEEE Transactions on Image Processing

New neutrosophic approach to image segmentation

Pattern Recognition
ACO-based Projection Pursuit clustering algorithm

CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction

Signal Processing
A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation

Signal Processing
Image Segmentation Based on Bacterial Foraging and FCM Algorithm

International Journal of Swarm Intelligence Research

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper presents a fuzzy clustering approach for image segmentation based on Ant-Tree algorithm, which is inspired from the ants' self-assembling behavior. Three features including the gray value, gradient and neighborhood of pixels are extracted for clustering. A three-level tree model is proposed to make the clustering structure more adaptive for image segmentation. Center approximation is employed to optimize the fuzzy clustering process when building the tree structure. Besides, we present a new initialization method by making use of the histogram of the image. Experiments and comparisons show the effectiveness and the efficiency of the proposed approach.