Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Expert system technology: development and application
Expert system technology: development and application
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using simulated annealing to design good codes
IEEE Transactions on Information Theory
Robot Vision
Digital Picture Processing
The Schema System
Image Interpretation Using Bayesian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic independence networks for hidden Markov probability models
Neural Computation
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Joint Segmentation and Image Interpretation Using Hidden Markov Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
A novel pixon-representation for image segmentation based on Markov random field
Image and Vision Computing
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Design and implementation of cost-effective probabilistic-based noise-tolerant VLSI circuits
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Markov random field approach to region extraction using Tabu Search
Journal of Visual Communication and Image Representation
Scale selection for anisotropic diffusion filter by Markov random field model
Pattern Recognition
Context modeling in computer vision: techniques, implications, and applications
Multimedia Tools and Applications
An HMM-SVM-based automatic image annotation approach
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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An image is segmented into a collection of disjoint regions that form the nodes of an adjacency graph, and 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. The interpretation labels are modeled as a Markov random field (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. 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 MRF. A general methodology is provided for the design of the clique functions. Results of image interpretation experiments on synthetic and real-world images are described.