Double weighting convolutional neural net‐works for multi‐view 3D shape recognition

Abstract

Three‐dimensional (3D) object recognition based on multiple views has been a popular area of research in recent years. Existing methods based on the grouping mechanism cannot sensibly group the views. Thus, the 3D shape descriptor that is generated by the final fusion is not representative, and the recognition accuracy still requires improvement. This study proposes a double‐weighting convolutional neural network method, based on the L2‐S grouping mechanism. The designed bidirectional long short‐term memory module can learn the relationship between the views in detail and improve the quality of the extracted features. Further, the proposed L2‐S grouping mechanism can use the L2 norm property to calculate the discrimination score of views and group views more reasonably. After reasonable grouping, weighted fusion operations are used within and between groups to fuse features to obtain group‐level descriptors that better represent each group of views. Finally, compact 3D shape descriptors generated by equally important group‐level descriptors for 3D object recognition. Results of the experiments show that our method can achieve state‐of‐the‐art performance. The source code is available at https://github.com/Qishaohua94/DWCNN.

KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis Classification

“…In the first scenario, the new dataset is small and similar to the original dataset. In the second scenario, the dataset is large but similar to the original dataset [36], and CNN is preferred as a fixed feature extractor method. In the third scenario, the novel dataset is large and differs from the original dataset, and the CNN method is preferably fine-tuned.…”