Markov random field model-based approach to image interpretation

J. Zhang, J. W. Modestino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


In this paper, a Markov random field (MRF) model-based approach to automated image interpretation is described and demonstrated as a region-based scheme. In this approach, an image is first segmented into a collection of disjoint regions which form the nodes of an adjacency graph. Image interpretation is then 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. The interpretation labels are modeled as a MRF on the corresponding adjacency graph and the image interpretation problem is formulated as a maximum a posteriori (MAP) estimation rule. Simulated annealing is used to find the best realization, or optimal MAP interpretation. Through the MRF model, this approach also provides a systematic method for organizing and representing domain knowledge through the clique functions of the pdf of the underlying MRF. Results of image interpretation experiments performed on synthetic and real-world images using this approach are described and appear promising.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsWilliam A. Pearlman
Place of PublicationBellingham, WA, United States
PublisherPubl by Int Soc for Optical Engineering
Number of pages12
Volume1199 pt 1
ISBN (Print)0819402389
StatePublished - Dec 1 1989
EventVisual Communications and Image Processing IV - Philadelphia, PA, USA
Duration: Nov 8 1989Nov 10 1989


OtherVisual Communications and Image Processing IV
CityPhiladelphia, PA, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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