Constallation Models for Sketch Recognition

D. Sharon and M. van de Panne

Summary

The authors introduce a vision-based technique for sketch recognition that is based on invidiual features such as shape and size of a stroke and pair-wise features such as distances to other parts of a known sketch. The model determines a four-element feature vector for individual parts a four-element feature vector for paired parts. The matching of a label to a stroke is given a likelihood value based on a energy function derived from the differences in individual and pair-wise feature vectors between the label and the stroke. The authors run multiple passes on a branch-and-bound search tree to find the maximum likelihood value to label a stroke.

The authors evaluated the speed of their approach with and without using doing multiple passes on different classes.

Discussion

I like this approach because it gives a large amount of freedom in how a sketch can be drawn. One downside of this approach is that while sketches can be drawn differently they still require the same amount of strokes. This seems like a huge limitation if one of your biggest benefits is freedom of form. My future work would be directed towards alleviating this issue.