Multimodal feature fusion for 3D shape recognition and retrieval

Three-dimensional shapes contain different kinds of information that jointly characterize the shape. Traditional methods, however, perform recognition or retrieval using only one type. This article presents a 3D feature learning framework that combines different modality data effectively to promote the discriminability of unimodal features. Two independent deep belief networks (DBNs) are employed to learn high-level features from low-level features, and a restricted Boltzmann machine (RBM) is trained for mining the deep correlations between the different modalities. Experiments demonstrate that the proposed method can achieve better performance.