Composite Sketch Shape Recognition Based on Dagsvm and Decision Tree
Dalian, China（2006）
摘要
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
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关键词
dagsvm,computational cost,primitive shape recognition,directed acyclic graphs svm (dagsvm),spatial constraints,learning (artificial intelligence),directed acyclic graph,feature-based recognition,sketch recognition,image recognition,similarity-based recognition,composite shape recognition,feature extraction,support vector machine,directed graphs,decision tree,semantic processing,decision trees,support vector machines,learning artificial intelligence
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