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.