Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision. The rise of large-scale datasets, which constitute a dense sampling of diverse real-world scenes, and the renaissance of deep learning techniques, which learn powerful feature representations directly from big raw data, have been bringing remarkable progress in the field of scene representation and classification. To help researchers master needed advances in this field, the goal of this paper is to provide a comprehensive survey of recent achievements in scene classification using deep learning. More than 260 major publications are included in this survey covering different aspects of scene classification, including challenges, benchmark datasets, taxonomy, and quantitative performance comparisons of the reviewed methods. In retrospect of what has been achieved so far, this paper is concluded with a list of promising research opportunities.
翻译:旨在通过理解整个图像将场景图像分类为预先界定的场景类别之一的场景分类,是计算机视觉中的一个长期、根本性和具有挑战性的问题。大规模数据集的崛起,构成对不同现实世界场景的密集抽样,以及深层次学习技术的复兴,直接从大原始数据中吸取了强有力的特征表现,在现场展示和分类领域取得了显著进展。为了帮助研究人员掌握该领域需要的进展,本文件的目标是利用深层学习对近期在现场分类方面的成就进行综合调查。本调查包括260多份主要出版物,涉及现场分类的不同方面,包括挑战、基准数据集、分类和对所审查方法的定量业绩比较。回顾迄今为止取得的成就,本文件最后列出了一份有希望的研究机会清单。