Stochastic Diffusion for Correspondence Estimation and Objects Segmentation

In this paper, a generative model combined with stochastic framework is proposed and applied to the simultaneous correspondence estimation and object segmentation. The correspondence and segment fields are explicitly modelled as Markov random fields, and estimated in the maximum a posteriori framework. Some stochastic models are defined as the potential functions to reflect the interaction of the fields. The potential functions of the fields are stochastically diffused with the probability distributions of the neighboring fields, and the probability spaces of the fields are updated from the diffused potential spaces. The stochastic diffusion proposed as an energy minimization process is a kind of generative model which updates and regenerates the probability spaces of the correspondence and segment fields. Some experiments are performed on the simultaneous correspondence estimation and object segmentation. The results show stable and good performances in estimating the correspondence fields and extracting the objects in the scene.