基于卷积神经网络的中国绘画图像分类  

Chinese Painting Image Classification Based on Convolution Neural Network

作　　者：杨冰[1] 陈浩月 王小华[1] 姚金良[1] YANG Bing;CHEN Hao-yue;WANG Xiao-hua;YAO Jin-liang(Institute of Cognitive and Intelligent Computing,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学认知与智能计算研究所,浙江杭州310018

出　　处：《软件导刊》2019年第1期5-8,共4页Software Guide

基　　金：国家自然科学基金项目(61402143)

摘　　要：绘画图像分类为绘画管理与使用提供了便利。传统图像分类大多依靠人工提取形状、颜色等特征,由于绘画图像分类需要更专业的知识背景,从而使人工提取特征的过程繁琐且复杂。基于此,提出一种基于卷积神经网络的中国绘画分类方法,并在此基础上结合SoftSign与ReLU两种激活函数的优点,构造一种新的激活函数。实验结果表明,基于改进后激活函数构造的卷积神经网络,可以有效提高分类准确率。The classification of painting images facilitates the management and use of paintings.Different from traditional image classification,features such as artificial extraction of shapes and colors are required.Classification of painting images requires a more professional knowledge background,which also makes the process of manually extracting features increasingly complicated.Based on this,a Chinese painting classification method based on convolutional neural network is proposed.Based on this,it combines the advantages of two activation functions including SoftSign and ReLU to construct a new activation function.Experimental results show that the convolutional neural network constructed based on the improved activation function can effectively improve the classification accuracy.

关 键 词：深度学习 卷积神经网络 中国绘画 激活函数 图像分类 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]