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.

中文翻译：

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使用预训练的深度卷积神经网络对红绿蓝深度图像进行分类

摘要

图像分类是计算机视觉领域面临的严峻挑战之一，它还为其他任务（例如图像字幕，目标检测，图像着色等）奠定了基础。卷积神经网络（CNN）技术具有以下潜力：完成各种数据集的图像分类。随着技术的进步，相机正在捕获诸如深度之类的高级信息。因此，将深度信息合并到CNN中以提供更好的图像分类体验至关重要。本文尝试对华盛顿RGB-D（RGB-Depth）数据集采用经过预训练的GoogLeNet。此外，对GoogLeNet的深度数据进行了评估，该深度数据对RGB-D数据集提供了合理的分类率。此外，