Interactive Learning of Structural Shape Descriptions from Automatically Generated Near-miss Examples

Tracy Hammond and Randall Davis

Summary

Hammond and Davis present an approach to fix over- and under-constrained shape definitions for a shape description language. The authors developed the approach for the LADDER shape language. Their approach requires a positive hand-drawn example and shape description that will properly recognize the provided example. Over-constrained descriptions are checked by sequentially negating each constraint and constructing a near-miss shape that tests the constraint. Under-constrained descriptions are checked similarly, except that negated constraints are added instead of checking existing constraints.

Discussion

Having used LADDER, the ability to uncover under- and over-constrained descriptions is incredibly valuable. It's very easy in LADDER to under-constrain a description.