Non-parametric and unsupervised Bayesian classification with Bootstrap sampling

摘要

In this paper, we propose a non-parametric and unsupervised Bayesian classification based on the principle of Bootstrap sampling (BS) which reduces the dependence effect of pixels in real images, and reduces the classification time. Given an original image, we randomly select a small representative set of pixels. Then, a Non-parametric Expectation-Maximization (NEM) algorithm is used for image segmentation. The non-parametric aspect comes from the use of the orthogonal probability density function (pdf) estimation, which is reduced to the estimation of the first Fourier coefficients (FC's) of the pdf with respect to a given orthogonal basis. The results we obtain show that the BS method gives better results than the classical one, both in the quality of the segmented image and the computing time.