A 3D model retrieve method integrating shape distribution and self-organizing feature map

Meifa Huang; Hui Jing; Yanru Zhong; Bing Kuang

doi:10.1117/12.755925

28 November 2007 A 3D model retrieve method integrating shape distribution and self-organizing feature map

Meifa Huang, Hui Jing, Yanru Zhong, Bing Kuang

Proceedings Volume 6833, Electronic Imaging and Multimedia Technology V; 683309 (2007) https://doi.org/10.1117/12.755925
Event: Photonics Asia 2007, 2007, Beijing, China

Abstract

Shape Distribution is fast, simple, and robust method in 3D model retrieve. This method, however, only considers distances between the objects' shape distribution histograms and ignores the information included. As the result, the retrieval precision is low. To enhance the retrieve efficiency, a novel method which integrates Shape Distribution and Self-Organizing Feature Map (SOFM) is proposed. The models' shape distribution histograms are established by Shape Distribution and transformed into the proper format of SOFM. The similar models are grouped in neighboring neurons of SOFM by using competitive learning approach. In addition, the dissimilar models are indexed in far away neurons. With the given query model, SOFM classifies it into the proper cluster and exports the retrieval results. A case study is presented and the results show that the retrieval precision of the proposed method is higher than that of the Shape Distribution method.

Citation Download Citation

Meifa Huang, Hui Jing, Yanru Zhong, and Bing Kuang "A 3D model retrieve method integrating shape distribution and self-organizing feature map", Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 683309 (28 November 2007); https://doi.org/10.1117/12.755925

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KEYWORDS

3D modeling

Data modeling

Databases

Neurons

Feature extraction

Performance modeling

Visual process modeling

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