  The design, analysis and application of a volumetric convolutional neural
network (VCNN) are studied in this work. Although many CNNs have been proposed
in the literature, their design is empirical. In the design of the VCNN, we
propose a feed-forward K-means clustering algorithm to determine the filter
number and size at each convolutional layer systematically. For the analysis of
the VCNN, the cause of confusing classes in the output of the VCNN is explained
by analyzing the relationship between the filter weights (also known as anchor
vectors) from the last fully-connected layer to the output. Furthermore, a
hierarchical clustering method followed by a random forest classification
method is proposed to boost the classification performance among confusing
classes. For the application of the VCNN, we examine the 3D shape
classification problem and conduct experiments on a popular ModelNet40 dataset.
The proposed VCNN offers the state-of-the-art performance among all
volume-based CNN methods.
