Mean shift algorithm equipped with the intersection of confidence intervals rule for image segmentation

Authors:
Peng Tie-Gen;Wang Yin-Hua;Wu Ti-Hua
Affiliations:
Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China;School of Chemistry and Chemical Technology, Shanghai Jiaotong University, Shanghai 200240, China;Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China
Venue:
Pattern Recognition Letters
Year:
2007

Citing 6
Cited 1

Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel density estimation with adaptive varying window size

Pattern Recognition Letters
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernal density estimation

Journal of Multivariate Analysis
A new method for varying adaptive bandwidth selection

IEEE Transactions on Signal Processing
The estimation of the gradient of a density function, with applications in pattern recognition

IEEE Transactions on Information Theory

Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.10

Visualization

Abstract

The mean shift algorithm equipped with the intersection of confidence intervals (ICI) rule for image smoothing and segmentation is proposed. Firstly, the ICI rule for bandwidth selection in a multi-dimensional feature space is studied. In the ICI rule, the kernel function is used to estimate the probability density intervals of the pixel feature and find its bandwidth close to the optimal parameter. Secondly, the mean shift algorithm is used for image smoothing and segmentation with the bandwidth determined by the ICI rule. Experimental results show that the structures of the objects in images are preserved and over-segmentation caused by noises and texture can be eliminated effectively. In addition, a comparison between the smoothing results with adaptive bandwidths determined by the ICI rule and with fixed bandwidths is done. The results show that the proposed method is better in the field of image smoothing and segmentation.