012 - Learning of Shape Descriptions

Summary:

The paper is mostly concerning constraints on different features. The authors state that users pay more attention to some features and less to others. It introduces a score to different constraints, and allows for different settings on constraints. The best example is the parallel lines - when parallel lines are close in proximity, people recognize them as parallel lines very quickly. If the lines are far apart, it becomes less likely to be recognized, and by other gestures in between the lines human recognition drops drastically. In essence, their system was based as close to real humans as possible, not as close to the ideal recognition a computer can make.

Discussion:

I like the paper quite a bit, though I think it's somewhat unnecessary. I think the whole point of recognizers should be to get as close to human recognition as possible. In this day and age, we've proven that computers can think faster than any human being except on simple math problems and some seemingly unsolvable computer problems. I read that the human brain has around one terrabyte of memory, which you can go out and buy in a single hard drive in today's market. In that sense, we shouldn't shoot for perfect recognition in these sketches, because sketches are human made and thus not perfect. Again, I think the paper just stated what everyone who built recognizers should be thinking - make it as human as possible.