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计算机系统应用:2018,27(12):25-32
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基于改进CNN特征的场景识别
薄康虎, 李菲菲, 陈虬
(上海理工大学 光电信息与计算机学院, 上海 200093)
Scene Recognition Algorithm Using Advanced CNN Features
BO Kang-Hu, LEE Fei-Fei, CHEN Qiu
(School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)
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投稿时间:2018-05-12    修订日期:2018-06-04
中文摘要: 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.
Abstract: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.
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基金项目:上海市高校特聘教授(东方学者)岗位计划(ES2015XX)
引用文本:
薄康虎,李菲菲,陈虬.基于改进CNN特征的场景识别.计算机系统应用,2018,27(12):25-32
BO Kang-Hu,LEE Fei-Fei,CHEN Qiu.Scene Recognition Algorithm Using Advanced CNN Features.COMPUTER SYSTEMS APPLICATIONS,2018,27(12):25-32

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