Reading #12. Constellation Models for Sketch Recognition. (Sharon)

Comment
Jonathan
Summary

The paper aims to recognize sketches by introducing constellation models from computer vision. It uses constellation models to develop probabilistic models with a multi-pass branch-and-bound search method for object sketches based on multiple example drawings. Constellation models are as the figure below.

constellation model for face sketch recognition

The core of the system is to match all parts of a new sketch with those of training examples. All parts of a sketch are divided into two categories, mandatory parts and optional parts. The mandatory parts are more important than optional parts when recognizing.

An object class model is represented by the distribution of features in object constellation models. The system adopts multivariable gaussian distribution to learn the model.  The quality function of a match is defined by

Then it adopts maximum likelihood search to find the most plausible labelling for all strokes that appear in the image. Also it adopts two search phase to reduce the time cost. The first one is to label strokes that correspond only to the mandatory object parts, and the second one is to use hard constraints.

The  system is tested on 5 classes of objects. No recognition rate is shown in the paper.

Discussion

It is a good idea to introduce constellation model into sketch recognition. The model is good representation of the relative information between any two parts. It will help us to recognize symbols in Project2, I guess.

But there are several problems in this article that make me skeptical of it. First, there is no recognition rate shown in the paper. Whether the algorithm is feasible can not be proved. Five classes are not a large number for sketch recognition, so the test on those classes seem unpersuasive. Second, features used in the paper seem too simple for sketch recognition. Only the centeroid, the diagonal and angle of the bounding box are not enough to recognize sketch.