双核压缩激活神经网络艺术图像分类  

Art image classification with double kernel squeeze-and-excitation neural network

作　　者：杨秀芹 张华熊[1] Yang Xiuqin;Zhang Huaxiong(School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学信息学院,杭州310018

出　　处：《中国图象图形学报》2020年第5期967-976,共10页Journal of Image and Graphics

基　　金：浙江省服装个性化定制协同创新中心项目(浙教高科[2016]63号);中国纺织工业联合会应用基础研究项目(J201802)。

摘　　要：目的 为了充分提取版画、中国画、油画、水彩画和水粉画等艺术图像的整体风格和局部细节特征,实现计算机自动分类检索艺术图像的需求,提出通过双核压缩激活模块(double kernel squeeze-and-excitation,DKSE)和深度可分离卷积搭建卷积神经网络对艺术图像进行分类.方法 根据SKNet(selective kernel networks)自适应调节感受野提取图像整体与细节特征的结构特点和SENet(squeeze-and-excitation networks)增强通道特征的特点构建DKSE模块,利用DKSE模块分支上的卷积核提取输入图像的整体特征与局部细节特征;将分支上的特征图进行特征融合,并对融合后的特征图进行特征压缩和激活处理;将处理后的特征加权映射到不同分支的特征图上并进行特征融合;通过DKSE模块与深度可分离卷积搭建卷积神经网络对艺术图像进行分类.结果 使用本文网络模型对有无数据增强(5类艺术图像数据增强后共25 634幅)处理的数据分类,数据增强后的分类准确率比未增强处理的准确率高9.21%.将本文方法与其他网络模型和传统分类方法相比,本文方法的分类准确率达到86.55%,比传统分类方法高26.35%.当DKSE模块分支上的卷积核为1×1和5×5,且放在本文网络模型第3个深度可分离卷积后,分类准确率达到87.58%.结论 DKSE模块可以有效提高模型分类性能,充分提取艺术图像的整体与局部细节特征,比传统网络模型具有更好的分类准确率.Objective The development of online digital media technology has promoted the sharing and spreading of natural art images. However,given the increasing number of art images,effective classification and retrieval are urgent problems that need to be solved. In the face of massive art image data,problems may occur in traditional manual feature extraction methods,such as tagging errors and subjective tagging. Moreover,the professional requirements of classifiers are relatively high. Convolutional neural networks (CNNs) are widely used in image classification because of its automatic feature extraction characteristics. Most of these network models are used for feature extraction in key areas of photographed images. However,natural art images are different from photographed images. Specifically,the distribution of overall style features and local detail features is evidently uniform. Selective kernel networks (SKNet) can adaptively adjust their receptive field size according to the input image to select multi-scale spatial information. However,the softmax gating mechanism in the module only strengthens the dependence between the channels of the feature map after the convolution operation of the receptive field with large response to stimulus. It also ignores the role of local detail features. Squeeze-and-excitation networks (SENet) can enhance the features in different channels but cannot extract the overall features and local detail features of the input. To fully extract and enhance the overall style features and local detail features of art images and realize the automatic classification and retrieval of art images,we combine the characteristics of SKNet and SENet to build a block called double kernel squeeze-and-excitation (DKSE) module. DKSE blocks and depthwise separable convolutions are mainly used to construct a CNN to classify art images. Method SKNet can capture the overall features and local detail features with different scales. According to the multi-scale structural characteristics of SKNet,we build the D

关 键 词：艺术图像分类 深度可分离卷积 卷积神经网络 整体特征 局部细节特征 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]