Sam
Summary:
This paper discusses a spatial approach to the grouping and recognition of sketches. The process, as shown in the stolen image below, can be done in real-time. Strokes near each other are shown with the labeled graph in (b). Shapes are computed and matched to templates in (d). Templates return potential scores (0 to 1) which are used to determine best overall classification for the user's strokes.
I know what you're thinking... isn't speed an issue here?! The neighborhood graph in (b) helps to eliminate possible classifications based on vertex count and proximity. The authors also discredit potential strokes consisting of K components, where K is the number of strokes in the current largest template. Oh, and everything is based on machine learning (including the A* search). A user need only provide examples.Discussion:
AdaBoost sounds like a deliciously nerdy energy drink. As the authors discuss in their... Discussion... an off the shelf system that is both efficient and accurate would be boss. If this work could be furthered to achieve similar results with fewer templates, then Rubine himself might rejoice and raise an AdaBoost toast to designer-accessible sketch recognition plug-ins.
I am doubt about the system's performance. Even using a* or dynamic the search space still very large. I cannot belive this can be done in real time with high accuracy.
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