Unsupervised model-based object recognition by parameter estimation of hierarchical mixtures

Abstract

Model-based joint segmentation and recognition of objects is proposed in the framework of parameter estimation of hierarchical mixture densities. The maximum a posteriori (MAP) estimate of the parameters is computed by the application of a modified version of the expectation-maximization algorithm (EM with regularizing constraints applied to multiple level hierarchies). The approach is flexible in the sense that it allows for non-stationary pixel statistics, different noise models and is translation and scale invariant. Simulation results suggest that the scheme is well suited for recognition of partially occluded objects and recognition in complex and poorly modeled background.