Composite Sketch Shape Recognition Based on Dagsvm and Decision Tree

Sketch recognition provides the basis for semantic processing in sketching understanding, and it consists of two sequential and cyclic phases: primitive shape recognition and composite shape recognition. In this paper, a composite shape recognition algorithm based on support vector machines (SVM) and decision tree is proposed. Directed acyclic graphs SVM (DAGSVM) is used for primitive shape recognition and composite shape recognition. The decision tree is introduced to pre-classify the composite shape and to reduce the computational cost of recognition. The algorithm integrates the advantages of feature-based and similarity-based recognition approaches, and can deal with sketching sequence properly. Experiment demonstrates that the model is feasible