Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic way for region-based image segmentation based on Markov Random Field model
Pattern Recognition Letters
A semantics-based decision theory region analyzer
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Image Segmentation Algorithms Using the Pareto Front
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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In this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segmented into a set of disjoint regions by a conventional region-based segmentation technique which operates on image pixels, and a Region Adjacency Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our scheme then proceeds on the RAG by defining the region merging and labeling problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function. We exploit the proposed scheme to segment and interpret natural outdoor scene images.