Image classification using ensemble algorithms with deep learning and hand-crafted features

Abstract

In this study, ensemble learning based image classification method is proposed by using both features extracted by means of pre-trained convolutional neural networks (CNN) and hand-crafted. Recently, deep learning models have been widely used in computer vision applications and significantly increase performance. In this scope, classification process is performed by adding 4 hand-crafted features to 4096 deep learning features on the CIFAR-10 dataset. The contribution to the performance of system is measured by using both hand-crafted and deep learning features together. Classification accuracy rate is used as the performance criterion. Experimental studies show that the developed method gives better results than only using the deep learning features.