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

Comments

Akshay's Blog

Summary

This paper talks about an improvement in the LADDER system which is not related to the recognition of sketch but an improvement in the usability of the LADDER system. LADDER required users to provide shape descriptions for the sketch recognition. With a large vocabulary of constraints and shapes it becomes a difficult task for the user to write those description accurately.

When defining constraints either manually be developer or generated by the system. They can become over or under constrained resulting in false positives or false negatives. Here the author uses a near-miss strategy to help correct over and under constrained descriptions. For over constrained descriptions it removes a constraint and generates a shape which takes advantage of the removed constraint. The user than provides a feedback whether this shape is acceptable or not. Similarly for under-constrained descriptions it adds a constraint and generated a shape demonstrates the effect of the modification.

Discussion

A very useful feature added to the LADDER framework makes it more easier for the user to write the descriptions by visually seeing the possibilities and effect of constraints rather than to extensively think for each description and possibilities.

I think the near-miss strategy works by adding/removing one constraint. What happens if the description is under/over constrained by more than on constraint? Is the process iterative?