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The purpose of the study is to analyse and compare the most common machinelearning and deep learning techniques used for computer vision 2D objectclassification tasks. Firstly, we will present the theoretical background ofthe Bag of Visual words model and Deep Convolutional Neural Networks (DCNN).Secondly, we will implement a Bag of Visual Words model, the VGG16 CNNArchitecture. Thirdly, we will present our custom and novice DCNN in which wetest the aforementioned implementations on a modified version of the BelgiumTraffic Sign dataset. Our results showcase the effects of hyperparameters ontraditional machine learning and the advantage in terms of accuracy of DCNNscompared to classical machine learning methods. As our tests indicate, ourproposed solution can achieve similar - and in some cases better - results thanexisting DCNNs architectures. Finally, the technical merit of this article liesin the presented computationally simpler DCNN architecture, which we believecan pave the way towards using more efficient architectures for basic tasks.

Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification