A new Laplacian mixture conditional random field model for image labeling

Abstract

In this paper we present a novel conditional random field (CRF) model based on Laplacian mixtures for image labeling. Nature images posses many spatial regularities that can be efficiently modeled by probabilistic graphical models such as CRF. Usually hundreds of features and several types of feature functions are used together which increases computational complexity and makes the training difficult to converge. We propose a new Laplacian mixture CRF model, which simplifies the training and inference process without losing labeling accuracy. The belief propagation inference and stochastic gradient descent training are formulated accordingly for the new model. The experimental results demonstrate that the new approach achieves better classification accuracy than the baseline CRF and comparable results with the state-of-the-art complex models.