3D Model Retrieval Based on Semantic and Shape Indexes

Abstract: The size of 3D models used on the web or stored in databases is becoming increasingly high. Then, an efficient method that allows users to find similar 3D objects for a given 3D model query has become necessary. Keywords and the geometry of a 3D model cannot meet the needs of users' retrieval because they do not include the semantic information. In this paper, a new method has been proposed to 3D models retrieval using semantic concepts combined with shape indexes. To obtain these concepts, we use the machine learning methods to label 3D models by k-means algorithm in measures and shape indexes space. Moreover, semantic concepts have been organized and represented by ontology language OWL and spatial relationships are used to disambiguate among models of similar appearance. The SPARQL query language has been used to question the information displayed in this language and to compute the similarity between two 3D models. We interpret our results using the Princeton Shape Benchmark Database and the results show the performance of the proposed new approach to retrieval 3D models. Keywords: 3D Model, 3D retrieval, measures, shape indexes, semantic, ontology