Scene classification with improved AlexNet model
Scene classification with improved AlexNet model
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DOI：
10.1109/iske.2017.8258820
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发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
Lisha Xiao;Qin Yan;Shuyu Deng
中科院分区：
文献类型：
--
作者：
Lisha Xiao;Qin Yan;Shuyu Deng
场景分类是图像理解的一个重要研究分支，它模仿人类的生物系统，从图像中提取信息，并利用计算机系统进行解释。AlexNet模型在图像分类中受到限制，因为第一卷积层中的大卷积核和步幅导致特征图分辨率过快下降和空间信息过度压缩。根据卷积神经网络（CNN）的设计原理，提出了一种改进的AlexNet模型。大卷积核被分解成两个小卷积核级联的结构，具有减小的步幅。在第一个卷积层之后添加另一个卷积层，以增强低级特征或空间信息的集成过程。非对称卷积核应用于最后三个卷积层。在两个数据集上的实验表明，对于23类场景分类，改进后的AlexNet模型的分类准确率高于AlexNet模型和ZFNet模型。
Scene classification is an important research branch of image comprehension, which gains information from images and interprets them using computer system by imitating the biological systems of human beings. AlexNet model is limited in image classification because of the large convolution kernel and stride in the first convolutional layer leading to over rapid decline of feature maps resolution and excessive compression of spatial information. This paper proposed an improved AlexNet model according to the design principle of convolutional neural networks (CNNs). The large convolution kernel is decomposed into a structure cascaded by two small convolution kernels with reduced stride. Another convolutional layer is added after the first one to enhance the integration process of the low-level features or the spatial information. The asymmetric convolution kernel is applied in the last three convolutional layers. The experiments on two datasets show that the classification accuracy of the improved AlexNet model is higher than those of AlexNet model and ZFNet model for 23 categories of scene classification.