Sketch Recognition via Part-based Hierarchical Analogical Learning

32nd International Joint Conference on Artificial Intelligence (IJCAI)

Publication date: August 21, 2023

Kezhen Chen, Kenneth Forbus, Balaji Vasan Srinivasan, Niyati Chhaya, Madeline Usher

Sketch recognition has been studied for decades, but it is far from solved. Drawing styles are highly variable across people and adapting to idiosyncratic visual expressions requires data-efficient learning. Explainability also matters, so that users can see why a system got confused about something. This paper introduces a novel part-based approach for sketch recognition, based on hierarchical analogical learning, a new method to apply analogical learning to qualitative representations. Given a sketched object, our system automatically segments it into parts and constructs multi-level qualitative repre-sentations of them. Our approach performs analog-ical generalization at multiple levels of part de-scriptions and uses coarse-grained results to guide interpretation at finer levels. Experiments on the TU Berlin dataset and the Coloring Book Objects dataset show that the system can learn explainable models in a data-efficient manner.