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

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.