Reading #12

Comments:
Wenzhe

Summary

In general this paper used a template-based method, however, this template is built by learning a bunch of labeled data. And the template built from these models is a statistical model that capturing the distances between each sketch part in the model. And as for the sketch parts, some mandatory parts must exist in the sketch, while some optimal parts which can vary in different examples. This is one strong assumption in the paper.

For the recognition, first is to label is to label the mandatory parts, second is the search for the optional parts in the remaining strokes. Both of these searches uses the same objective function, which is a maximum likelihood. And here comes the second strong assumption that all the strokes for similar parts should be drawn in similar strokes, which will make the mapping much easier.

Comments:

I am really glad to see some paper used a more traditional Computer Vision method, where a statistical model is built for the shape/sketch. However, these two assumptions are the major concerns.

The assumption that mandatory parts mush exist is ok for me, since for example a human face must contain eyes, nose, ears, etc. This assumption is natural. However, the assumption that requires all the strokes draw similarly has added a big constrain for the user. Like a flowerpot that is drawn with one stroke should not be drawn with four separate strokes. A traditional shape model won't put any constrain on drawing order. This has lowered the difficulty to recognize each parts.