Comparison Optimization for Image Classification based on Deep Belief Network
Daohan Yang, Xiao Lan Yao, Wensong Bai, Bin Wang, Runyu Wang, Zuxi Zhang · 2018
In the field of intelligent robotics and control engineering，image classification technology based on deep learning is of great significance for robot image identification and has gained a wider range of applications.However, when it comes to the actual working environment of the robot, the existences of light illumination, noise and other factors will make the result of deep learning frame merely not satisfied.This paper focuses on the comparison of different feature extraction methods for optimization of DBN(Deep Belief Networks).Experimental results show that these methods can improve the accuracy of image classification.