Red Green Blue Depth Image Classification Using Pre-Trained Deep Convolutional Neural Network

Abstract

Image Classification is one of the eminent challenges in the field of computer vision, and it also acts as a foundation for other tasks such as image captioning, object detection, image coloring, etc. The Convolutional Neural Networks (CNN) techniques have the potency to accomplish image classification for a variety of datasets. With the advancements in technologies, cameras are capturing high-level information such as depth. Therefore, it is essential to incorporate depth information into CNN to provide a better experience of image classification. In this paper, an attempt is made to adapt pre-trained GoogLeNet on Washington RGB-D (RGB-Depth) dataset. Moreover, GoogLeNet is evaluated on depth data that has provided reasonable classification rate on RGB-D dataset. In addition, the paper works on analyzing the impact of pre-processing or resizing of images and batch size on classification accuracy of the model.