A MARKOV RANDOM FIELD MODEL-BASED APPROACH TO IMAGE INTERPRETATION

In this paper, a Markov random field (MRF) model-based approach to automated image interpretation is described and demonstrated. This scheme is a region-based approach in which an image is first segmented into a collection of disjoint regions that form the nodes of an adjacency graph. Once the adjacency graph has been determined, image interpretation is achieved through assigning object labels (or interpretations) to the segmented regions (or nodes) using domain knowledge, extracted feature measurements, and spatial relationships between the various regions. In this approach, the interpretation labels are modeled as an MRF on the corresponding adjacency graph, and the image interpretation problem is then formulated as a maximum a posteriori (MAP) estimation rule given domain knowledge and region-based measurements. Simulated annealing is used to find this best realization or optimal MAP interpretation. Through the MRF model and its associated Gibbs distribution, this approach also provides a systematic method for organizing and representing domain knowledge through appropriate design of the clique functions describing the Gibbs distribution representing the pdf of the underlying MPF. We provide a general methodology for the design of the clique functions. Results of image interpretation experiments performed on synthetic and real-world images using this approach are described.