Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Constructing Flexible Dynamic Belief Networks from First-Order Probalistic Knowledge Bases
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Efficient analysis of complex diagrams using constraint-based parsing
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Belief Propagation and Revision in Networks with Loops
Belief Propagation and Revision in Networks with Loops
Hierarchical parsing and recognition of hand-sketched diagrams
Proceedings of the 17th annual ACM symposium on User interface software and technology
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
Multi-domain sketch understanding
Multi-domain sketch understanding
Automatically transforming symbolic shape descriptions for use in sketch recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
LADDER: a language to describe drawing, display, and editing in sketch recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Multimodal integration-a statistical view
IEEE Transactions on Multimedia
Automatically transforming symbolic shape descriptions for use in sketch recognition
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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This paper presents a novel form of dynamically constructed Bayes net, developed for multi-domain sketch recognition. Our sketch recognition engine integrates shape information and domain knowledge to improve recognition accuracy across a variety of domains using an extendible, hierarchical approach. Our Bayes net framework integrates the influence of stroke data and domain-specific context in recognition, enabling our recognition engine to handle noisy input. We illustrate this behavior with qualitative and quantitative results in two domains: hand-drawn family trees and circuits.