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.

Reading #11

Comments:
Longfei

Summary

Rather than a detailed algorithm, LADDER is a language to describe how sketched diagrams in a domain are drawn, displayed, and edited. And this description can be automatically transformed into domain specific shape recognizer, editing recognizers and shape exhibitors to use for sketch recognition domain.

The LADDER as a language itself consists of following components:  Shape definition, Language contents, and vectors.

And the whole recognition system consists of Recognition of primitive shapes, Recognition of domain shapes, Editing recognition and Constraint solver.

The constraints play an important role for the whole system. These constraints can be predefined or user-customized. These constraints limit as well as simplify the way of doing recognition. 

Discussion

This method is not a traditional recognition method, like learning features or templates. T
his is like what I have discussed in the previous blog -- the context given by user can be rich enough and well defined, so that, instead of extracting feature and template from input sketch, the system is more robust at interpreting the context.

Undoubtedly, LADDER is a rich and well-defined context, instead of being an augmented context to the traditional method, this idea has opened a new way of doing sketch recognition.