Convolutional neural networks for image classification

This paper describes a learning approach based on training convolutional neural networks (CNN) for a traffic sign classification system. In addition, it presents the preliminary classification results of applying this CNN to learn features and classify RGB-D images task. To determine the appropriate architecture, we explore the transfer learning technique called “fine tuning technique”, of reusing layers trained on the ImageNet dataset in order to provide a solution for a four-class classification task of a new set of data.