Semantic Based 3D Model Retrieval

Semantic based 3D model retrieval (SB3DMR) has attracted more and more research interests, and is a challenge research problem in the field of content based 3D model retrieval (CB3DMR). Current studies concentrate on the relevance feedback or supervised learning to reduce the semantic gap between 3D model low-level features and high-level semantic. In this paper, a new method in extracting semantic feature for 3D model is proposed. It can get high-level semantic information automatically from low-level. First, invariant descriptors are extracted from 3D models to efficient semantic annotation. An unsupervised learning method to describe the semantics of the 3D models is proposed. Second, and based on the resulting semantic annotation, 3D models are semantically classified. Finally, spatial relationships are used to derive other high-level semantic features to augment our knowledge base and to improve the retrieval accuracy. An ontology based 3D model retrieval framework is used to combine the semantic concepts and visual features and introduce the query by semantic example.