Dempster–Shafer evidence theory-based multi-feature learning and fusion method for non-rigid 3D model retrieval

摘要

This study introduces a novel multi-feature-based non-rigid three-dimensional (3D) model retrieval method. First, for each 3D model, compute the scale-invariant heat kernel signature (SI-HKS) descriptor and the wave kernel signature (WKS) descriptor of each vertex. Then, the normalised weighted bags of phrases feature is obtained and they are fed to the convolutional neural networks. The trust degree of each kind of descriptor is computed, and the total trust degree can be obtained. Finally, the fusion network is trained and the retrieval results can be obtained according to the ranking of the total trust degrees. For the training phase and the testing phase, the authors define different computation methods of the trust degrees and the total trust degrees. The Dempster-Shafer (DS) evidence-based total trust degrees are used not only in the feature layer but also in the decision layer. The final decision results of the total trust degrees are used in the process of the network learning. So the proposed method can make full use of the complementary information of the SI-HKS descriptor and the WKS descriptor. Extensive experiments have shown that the proposed multi-feature fusion method has better performance than a single feature-based method, and also outperforms other existing state-of-the-art methods.