基于并行深度卷积神经网络的图像美感分类  

Image Aesthetic Classification Using Parallel Deep Convolutional Neural Networks

作　　者：王伟凝[1] 王励[1] 赵明权[1] 蔡成加 师婷婷 徐向民[1] 

机构地区：[1]华南理工大学电子与信息学院,广州510641

出　　处：《自动化学报》2016年第6期904-914,共11页Acta Automatica Sinica

基　　金：国家自然科学基金(61171142;61401161);广东省自然科学基金(2015A030313212);广东省前沿与关键技术创新专项资金(重大科技专项)(2014B010111003;2014B010111006)资助~~

摘　　要：随着计算机和社交网络的飞速发展,图像美感的自动评价产生了越来越大的需求并受到了广泛关注.由于图像美感评价的主观性和复杂性,传统的手工特征和局部特征方法难以全面表征图像的美感特点,并准确量化或建模.本文提出一种并行深度卷积神经网络的图像美感分类方法,从同一图像的不同角度出发,利用深度学习网络自动完成特征学习,得到更为全面的图像美感特征描述;然后利用支持向量机训练特征并建立分类器,实现图像美感分类.通过在两个主流的图像美感数据库上的实验显示,本文方法与目前已有的其他算法对比,获得了更好的分类准确率.With the rapid development of computers and social networks, automatic image aesthetic evaluation is in demand and has attracted more and more attention recently. Since the complexity and subjectivity of image aesthetic evaluation task, the traditional handcrafted features and generic image descriptors are hard to represent the overall aesthetic character of images. It is difficult for them to quantify and model the image aesthetics exactly. In this paper,a new method of image classification based on parallel deep convolutional neural networks is proposed. We use parallel deep learning networks to automatically complete feature extraction and acquire more comprehensive description of image aesthetics from different views. Then a support vector machine（SVM） classifier is built with the aesthetic features to accomplish image aesthetic classification. Experiments on two most frequently used databases of image aesthetics demonstrate that our proposed method achieves better results than other exsiting methods.

关 键 词：图像美感评估 深度卷积神经网络 并行卷积神经网络 特征提取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]