A Fast Military Object Recognition using Extreme Learning Approach on CNN

Abstract

Convolutional Neural Network (CNN) is an algorithm that can classify image data with very high accuracy but requires a long training time so that the required resources are quite large. One of the causes of the long training time is the existence of a backpropagation-based classification layer, which uses a slow gradient-based algorithm to perform learning, and all parameters on the network are determined iteratively. This paper proposes a combination of CNN and Extreme Learning Machine (ELM) to overcome these problems. Combination process is carried out using a convolution extraction layer on CNN, which then combines it with the classification layer using the ELM method. ELM method is Single Hidden Layer Feedforward Neural Networks (SLFNs) which was created to overcome traditional CNN’s weaknesses, especially in terms of training speed of feedforward neural networks. The combination of CNN and ELM is expected to produce a model that has a faster training time, so that its resource usage can be smaller, but maintaining the accuracy as much as standard CNN. In the experiment, the military object classification problem was implemented, and it achieves smaller resources as much as 400 MB on GPU comparing to standard CNN.