Reading #12 Constellation Models for Sketch Recognition

Summary

This paper uses constellation model to capture structure of particular class of objects. Their algorithm try to assign labels to strokes according to the likelihood computed.

The constellation model in this paper classifies each label as mandatory or optional. They compute individual features for each part, but only compute pair wise features for mandatory parts to reduce calculation. The likelihood of labeling is computed and maximum likelihood search is used to find the best labeling for all strokes.  The authors also use multi pass threshold and hard constraints to avoid spend too much time on the process of maximum likelihood search.

Discussion

As the authors said, their system has a big limitation that each stroke is required to have a label. This requirement is similar with gesture recognition that asks users to draw a gesture using only one stroke. In addition, this paper doesn’t talk about its recognition accuracy rate and use too little test data, which makes its conclusion not very persuasive.Even though,using constellation models in sketch recognition is a different idea which may be helpful in recognize certain type of shapes.