3D object retrieval based on a graph model descriptor

Authors:
Qinkun Xiao;Haiyun Wang;Fei Li;Yue Gao
Affiliations:
Department of Electronics Information Engineering, Xi'an Technological University, Xi'an 710032, China;STMicroelectronics R&D of Asia-Pacific, Singapore 554574, Singapore;Fujitsu Research & Development Center Co., Ltd., Beijing 100025, China;Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Neurocomputing
Year:
2011

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Abstract

A new 3D object retrieval approach is proposed based on a novel graph model descriptor and a fast graph matching method. Our methodology is made up of two steps. Firstly, a Bayesian network lightfield descriptor (BLD) is built, based on graph model learning, to overcome the disadvantages of the existing view-based methods. The 3D object is put into the lightfield, multi-view images are obtained; then features of the new multi-view images are extracted. Based on the extracted features, a Bayesian network learning algorithm is used to construct the BLD. Secondly, the 3D object is efficiently retrieved, based on graph model matching and learning from relevant feedback. Experimental results demonstrate that our algorithm has better performance and efficiency than the existing view-based methods.