基于改进CNN特征的场景识别

随着人工智能的发展，场景识别作为计算机视觉研究的重要方向之一，吸引着越来越多研究者的关注.由于传统的手工特征无法充分描述场景图像的信息导致效果不理想，而卷积神经网络（CNN）提取的特征能够包含丰富的场景语义和结构信息，因此就常见的体系结构而言，本文选取AlexNet网络模型进行场景识别的研究，分别从网络模型的深度、宽度、多尺度化提取以及多层融合考虑进行改进，改进后在两个数据集上的识别率分别可达92.0%和94.5%，通过对比结果表明了本文方法的有效性.

Scene Recognition Algorithm Using Advanced CNN Features

With the development of artificial intelligence, scene recognition has attracted more and more researchers'' attention, which is one of the important directions of computer vision research. The traditional manual features cannot sufficiently describe the characteristics of the scene images, which leading to unsatisfied performance. On the contrary, the features extracted from Convolutional Neural Networks (CNN) contain rich semantics and structural information of the scene images. As one of the most common architectures, AlexNet network model is chosen in this study. By improving the following 4 aspects of the network:depth, width,multi-scale extraction, and multilayer fusion, the proposed approach achieves high accuracies of 92.0% and 94.5% on two publicly available datasets respectively, showing the superiority compared with other methods.