19 - Multiscale Models of Temporal Patterns

Summary:

Sezgin et al write about how temporal patterns in user drawing process can be used to determine recognition. The paper discusses the different equations that can be used to find patterns, as well as how Hidden Markov Models can be used coinciding with these equations to find possible recognition rates. Using both gives a decently accurate sample of what the user intends to draw, except for the 'transistor' sketch on a circuit diagram. This is usually caused by segmentation with a wire being drawn after the main part of the transistor and before the "rebounded" part of the transistor. Sezgin et al compensate for that, looking further in the drawing queue for similar examples, basically translating the wire after the "rebounded" part. Results are quite impressive, usually in the 80-90% correct recognition.

Discussion:

While I believe there is a future in this way of thinking, I'm not sure about the exact direction. The paper seems to rely heavily on time data, which is not a TERRIBLE idea but can lead to a dependent relationship between the computer and specific users. The idea that people draw things in a specific order is great in theory, but sketches are usually used in a design process, and when designers design something "new" they usually don't always think linearly. For example, and architect usually draws an outlying structure of a house before putting in the interior walls, to make sure everything fits. But when he gets free reign to build something from scratch, rooms will be continuously added to an existing building, changing the outer wall again and again. I don't think these problems would make the system completely useless, but more work would be needed to keep a domain-independent status.