Abstract With the success of deep learning in the field of computer vision, object recognition has made important breakthroughs, and its recognition accuracy has been drastically improved. However, the performance of scene recognition is still not sufficient to some extent because of complex configurations. Over the past several years, scene recognition algorithms have undergone important evolution as a result of the development of machine learning and Deep Convolutional Neural Networks (DCNN). This paper reviews many of the most popular and effective approaches to scene recognition, which is expected to create benefits for future research and practical applications. We seek to establish relationships among different algorithms and determine the critical components that lead to remarkable performance. Through the analysis of some representative schemes, motivation and insights are identified, which will help to facilitate the design of better recognition architectures. In addition, current available scene datasets and benchmarks are presented for evaluation and comparison. Finally, potential problems and promising directions are highlighted.

中文翻译：

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场景识别：综合调查

摘要 随着深度学习在计算机视觉领域的成功，物体识别取得了重要突破，其识别准确率得到了大幅提升。然而，由于配置复杂，场景识别的性能在一定程度上仍然不够。在过去几年中，由于机器学习和深度卷积神经网络 (DCNN) 的发展，场景识别算法经历了重要的演变。本文回顾了许多最流行和最有效的场景识别方法，有望为未来的研究和实际应用带来好处。我们寻求建立不同算法之间的关系，并确定导致卓越性能的关键组件。通过对一些代表性方案的分析，确定了动机和见解，这将有助于促进更好的识别架构的设计。此外，还提供了当前可用的场景数据集和基准以进行评估和比较。最后，强调了潜在的问题和有希望的方向。