Balanced principal component for 3D shape recognition using convolutional neural networks

Abstract

Currently, PCA (principal component analysis) is widely used in many neural networks and has become a crucial part of the convolutional neural network (CNN) feature extraction. However, whether PCA is suitable for this process remains to be elucidated. The authors proposed a new method called balanced principal component (BPC) that generates a balanced local feature and combines with CNN as a layer to cope with the fusion problem. Specifically, BPC layer includes regionalisation module and average compression PCA (AC‐PCA) module. First, they used regionalisation module to generate some sub‐region that focuses on the local feature in each view. Secondly, the AC‐PCA module is a computational process that enlarges the feature matrix by PCA and eventually compacts the matrix to a one‐dimensional (1D) vector by AC. Next, all 1D vectors are compacted by AC to obtain a multi‐dimensional balance. Finally, they designed this layer with an end‐to‐end trainable structure to promote the feature extraction task of CNN. They addressed 3D shapes using a projection method that is pre‐trained on ImageNet and migration learning on ModelNet dataset. By comparing with the state‐of‐the‐art network, they achieved a significant gain in performance of retrieval and classification tasks.