Joint segmentation and image interpretation using hidden Markov models

Abstract

Image interpretation consists of interleaving the low-level task of image segmentation and the high-level task of interpretation. The idea being that the interpretation block guides the segmentation block which in turn helps the interpretation block in better interpretation. In this paper we develop a joint segmentation and image interpretation scheme using the notion of joint hidden Markov model (HMM) for probabilistic modeling of spatial relationship. We find the optimal interpretation labels, which are nothing but the optimal state sequence of the HMM.