Comment:
Chris
Summary:
In this paper, the author presents a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagram. Their approach is completely spatial, and does not require any ordering on the strokes. Their implementation can be summarized as following.
1) Building building neighborhood graph. For graph (V,E), vertices are the individual stroks, and if two stroke are connected, then there will be edge between them. In order to determine if two strokes are connected, they use proximity distance measurement, they use threshold to control it. If distance between two stroks is less than the threshold, two vertices are connected.
2) The whole graph is divided into seperate subgraphs and each subgraph is recognized by recognizer. They will search through all these groupings and find optimal one. This is combinatorial problem which is computationaly very expensive. In this paper, they use some optimization techniques to decrease the complexity very much. There are two techniques used, the first one is they construct a neighborhood graph in which vertices that are colse to each other, the second one is to restrict the sizeof each subset to be less than a constant K. For searching, they used two approahces, dynamic programming and A* search. In this paper they mentioned the A* search approach.
3) The remaining thing is to build a recognizer, which is built using boosted decision trees. For avoiding overfitting, they used depth 3 decision trees.
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