View-Based 3D Model Retrieval by Joint Subgraph Learning and Matching

View-based 3D model retrieval is an important and challenging task in computer vision, which can be utilized in many applications such as landmark detection, image set classification, etc. Representation view selection and similarity measure are two key problem in view-based 3D model retrieval. Many classic algorithms were proposed to handle these two problems. However, they were often independent to consider these two problems while ignoring the contact with each other. In this paper, we proposed a joint subgraph learning & matching method (SGLM) via Markov Chain Monte Carlo (MCMC) to handle view-based 3D model retrieval problem, which effectively combine representation view extraction with similarity measure process to find the best matching result. The proposed (SGLM) can benefit: 1) considering the correlation between representation view selection and similarity measure, which can effectively improve the final performance of retrieval; 2) eliminating redundant visual information by subgraph learning; 3) learning representation views automaticly in similarity measure process. We validate the SGLM based on 3D model retrieval on ETH, PSB, NTU and MVRED datasets. Extensive comparison experiments demonstrate the superiority of the proposed method.