Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
