Analysis of the convolutional neural network architectures in image classification problems
Sergey C. Leonov, Alexander N. Vasilyev, Artyom Makovetskii, Kober Vitaly · 2019
The work aims to construct effective methods for image classification. For this purpose, we analyze neural network convolutional architectures, which understand as the number of network layers, elements in the input and output layers, the type of activation functions, and the connections between neurons. We studied the application of various configurations of convolutional networks for solving image classification problems. Numerical experiments on BOSPHORUS database were conducted; we described the results in this work. A neural network architecture has been developed based on the analysis of convolutional neural networks, which for the data set under consideration, provides the most accurate classification. A new method combines the advantages of using RGB images and depth maps as input data is proposed for processing the output of a convolutional network.