---
title: "Dynamically Constructed Bayes Nets for Multi-Domain Sketch Understanding"
authors:
  - "Christine Alvarado"
  - "Randall Davis"
doi: ""
url: "https://ijcai.org/proceedings/2005/198"
pdf: "https://ijcai.org/proceedings/2005/0198.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "1407-1411"
track: ""
keywords:
---

# Dynamically Constructed Bayes Nets for Multi-Domain Sketch Understanding

Christine Alvarado, Randall Davis

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 1407-1411.

## Abstract

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.