Joint segmentation and image interpretation

Interpreting images is a difficult task to automate. Image interpretation essentially consists of both low level and high level vision tasks. In this paper, we develop a joint scheme for segmentation and image interpretation in a multiresolution framework, where segmentation (low level) and interpretation (high level) interleave. The idea being that the interpretation block should be able to guide the segmentation block which in turn helps the interpretation block in better interpretation. We assume that the conditional probability of the interpretation labels, given the knowledge vector and the measurement vector is a Markov random field (MRF) and formulate the problem as a MAP estimation problem at each resolution. We find the optimal interpretation labels by using the simulated annealing algorithm. The proposed algorithm is validated on some real scene images