Bayesian learning for object based image segmentation
Zhen Jia, A. Balasuriya · 2005
This paper proposed an algorithm using Bayesian on-line learning for object based video image segmentation. First the strengths of image pixel's spatial location, color and motion segments are weighted and then unified in one framework for image clustering and segmentation. Here, the appropriate modeling of probability distribution functions (PDF) of each feature cluster is obtained through Gaussian distribution. In this paper, unsupervised Bayesian learning is implemented to identify these distribution parameters. The online Bayesian learning process is carried out with the previous clustered image pixels information and feature clusters Gaussian PDF information. This algorithm has shown good results on different video files.