Computer Science ›› 2016, Vol. 43 ›› Issue (7): 303-309.doi: 10.11896/j.issn.1002-137X.2016.07.056

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3D Model Retrieval Algorithm Based on Multi Feature Fusion

ZHOU Yan, ZENG Fan-zhi and YANG Yue-wu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: For the problem of the single feature retrieval effectiveness in 3D model retrieval,in this paper we proposed three kinds of feature vector extraction algorithms of 3D model which include Extended Gauss Sphere(EGS)feature vector of describing the surface characteristics of the model,Radon Transform Spherical Distribution (RTSD) feature vector of reflecting the internal structure of the model,and the View Hierarchical Compressed Sensing (VHCS) feature vector of representing projection layer of the model.Secondly,we presented a weighted coefficient estimation method for multi feature fusion based on the sample model query result classification information entropy and the supervised learning process.Finally,multi feature fusion similarity measure between models was designed to complete the 3D model retrieval based on query sample.Simulation results show that three kinds of feature vectors proposed have better distinguish ability,recall and precision of multi feature fusion retrieval algorithm are improved obviously.

Key words: 3D model retrieval,Feature fusion,Model similarity,Hierarchical compressive sensing

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