Non Rigid 3D Model Retrieval Method Based on Fisher Vector Encoding and Distance Learning

In order to solve the problem of insufficient representation of the non rigid 3 D feature descriptor, we propose a new method of non rigid 3 D model retrieval based on Fisher Vector coding and distance learning. Firstly, we extract the local descriptor of 3 D model to train the dictionary by using Gaussian mixture model; then, the local descriptor and the dictionary centers are used as inputs to learn the new feature codes by Fisher Vector encoding method; finally, we map the feature codes with distance learning aiming to construct an efficient global feature with a small inter-class margin and a large intra-class variance, which is used for non rigid 3 D model retrieval. We validate our method on the open data sets SHREC10 and SHREC11. The results show that the method achieves a higher accuracy than the traditional method.