Segmentation of nonrigid object in a nonparametric MAP framework

This paper presents an efficient segmentation approach for nonrigid video object. We propose to formulate the video object segmentation problem as the maximum a posteriori probability (MAP) problem and define the probabilistic models in terms of the object's density function. Furthermore, in order to accurately represent the density function for video object with arbitrary shape and complex texture, we employ a nonparametric method to estimate the density function. Our proposed density estimation mostly relies on the object's color features and requires no time-consuming motion estimation. In addition, we further employ an efficient mean-shift procedure in the MAP optimization step to largely reduce the computational cost. Our experiments demonstrate that the segmentation results are very promising even when the video objects are severely deformed or occluded.