Dynamically constructed Bayes nets for multi-domain sketch understanding

Authors:
Christine Alvarado;Randall Davis
Affiliations:
Harvey Mudd College, Claremont, CA and MIT, CSAIL, Cambridge, MA;MIT, CSAIL, Cambridge, MA
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

This paper presents a novel form of dynamically constructed Bayes net, developed for multidomain 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.