Composite Sketch Shape Recognition Based on Dagsvm and Decision Tree

Author

Liao, Shi-Zhong ; Liu, Wen-gang ; Guo, Wei

Author_Institution

Sch. of Comput. Sci. & Technol., Tianjin Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3254

Lastpage

3259

Abstract

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

Keywords

decision trees; directed graphs; feature extraction; image recognition; learning (artificial intelligence); support vector machines; DAGSVM; composite shape recognition; computational cost; decision tree; directed acyclic graph; feature-based recognition; primitive shape recognition; semantic processing; similarity-based recognition; sketch recognition; support vector machine; Artificial intelligence; Computational efficiency; Computer science; Concrete; Cybernetics; Decision trees; Documentation; Graphics; Machine learning; Shape; Strontium; Support vector machines; Composite shape recognition; Decision Tree; Directed Acyclic Graphs SVM (DAGSVM); Sketch Recognition; Spatial constraints;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258436

Filename

4028628