Fast view based 3D model retrieval via unsupervised multiple feature fusion and online projection learning
Since each visual feature only reflects a unique characteristic about a 3 dimensional (3D) model and different visual features have diverse discriminative power in model representation, it would be beneficial to fuse multiple visual features in 3D model retrieval. To this end, we propose a fast view based 3D model retrieval framework in this article. This framework comprises two parts: the first one is an Unsupervised Multiple Feature Fusion algorithm (UMFF) which is used to learn a compact yet discriminative feature representation from the original multiple visual features; and the second one is an efficient Online Projection Learning algorithm (OPL) which is designed to fast transfer the input multiple visual features of a newcome model into its corresponding low dimensional feature representation. In this framework, many existing ranking algorithms such as the simple distance based ranking method can be directly adopted for sorting all 3D models in the database using the learned new feature representation and returning the top ranked models to the user. Extensive experiments on two public 3D model databases demonstrate the efficiency and the effectiveness of the proposed approach over its competitors. The proposed framework cannot only dramatically improve the retrieval performance but also reduce the computational cost in dealing with the newcome models. HighlightsAn Unsupervised Multiple Feature Fusion (UMFF) algorithm is proposed to fuse multiple features for 3D model representation.The 1 graph method is used to learn roust and datum adaptive graph to preserve local geometric structure information.An efficient Online Projection Learning (OPL) algorithm is designed to solve the out of sample problem.
Signal Process.