A 3D Model Retrieve Method Integrating Shape Distribution and Self- Organizing Feature Map

摘要

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.