Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computational intelligence: a logical approach
Computational intelligence: a logical approach
Control of Selective Perception using Bayes Nets and Decision Theory
Control of Selective Perception using Bayes Nets and Decision Theory
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
On scene interpretation with description logics
Image and Vision Computing
Leveraging probabilistic season and location context models for scene understanding
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Use of context in vision processing: an introduction to the UCVP 2009 workshop
Proceedings of the Workshop on Use of Context in Vision Processing
Toward automated façades generation from oblique aerial images
UDMV '13 Proceedings of the Eurographics Workshop on Urban Data Modelling and Visualisation
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Appearance-based classification is a difficult task in many domains due to ambiguous evidence. Knowledge about the relationships between objects in the scene can help resolve this problem. In this paper, we present a new probabilistic classification framework based on the cooperation of decision trees and Bayesian Compositional Hierarchies, and show that introducing contextual knowledge in the form of dynamic priors significantly improves classification performance in the façade domain.