Computer Vision: Classification of Images Based On Deep Learning with the CNN Architecture Model

Abstract

Deep learning is a scientific field in Machine Learning (ML) that is developing with various applications, one of which is visual image processing technology. With the excellent capabilities of computer vision, image processing from computer visuals is used to duplicate the human ability to understand object information in the image. One of the Machine Learning (ML) methods that can be used for object classification in images is the Convolution Neural Network (CNN) method. The two core stages when processing object classification in the image, the first stage is image classification using feedforward, and the second stage applies the backpropagation method. In this study, before the classification stage, this method was first carried out through preprocessing, which is useful as an image separation to focus on the object to be classified. Furthermore, it is carried out by conducting pre-training using the feedforward method with the bias weights, which are updated after every training process. The observations of this study, the results of image classification training with a degree of ambiguity, resulted in a good average accuracy validation value of 0.91 in a confidence interval with a range of 0-1. So, it can be concluded that applying the Convolution Neural Network (CNN) method to distinguish objects in an image can classify them well.