Summary
In this work, the authors propose the use of constellation models in sketch recognition which is similar to the technique used in computer vision.
In their work, they label all the constituent parts of the sketch. For instance, it it is a sketch of a face, the objective is to find which part is the mouth, which is the nose, so on. To do so, they extract the parameters of the strokes distribution and the stroke pairs’ distribution from the training set. Next, to classify a sketch, all possible labeling are tried to find the one with maximum likelihood.
Discussion
The idea of using constellation model in sketch recognition is interesting however one limitation of this method is the requirement for each part of the sketch being labeled with e.g. eye, mouth, etc. I think they have made a big assumption about the availability of such tags.
Also LADDER can be thought of as a complement or a rival to this method as it can support more complex relationships, however in LADDER, relationships are defined whereas here, the relationships are learned.
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