Semantic 3D Model Retrieval Based on Semantic Tree and Shape Feature

3D model retrieval emerges as an important part of multimedia information retrieval. Current researches in 3D model retrieval concentrate on the shape-based way. However, its performance isn´t satisfying because of the semantic gap. The paper explores the semantic-based 3D model retrieval method based on semantic tree and the hybrid method based on content and semantic. First, the semantic tree is adopted to illustrate models´ semantic relationship and the accurate semantic of each tree node is described with several keywords. The semantic tree of all 1814 models of the Princeton Shape Benchmark is constructed. Second, the paper realizes the semantic retrieval based on the semantic tree which facilitates user´s feedback. Third, the hybrid retrieval process integrating semantic and content is proposed. The content similarity is computed by adopting the combined shape feature. The experiments conduct on Princeton Shape Benchmark shows the effectiveness of our method.