基于深度学习的图像美观度评价

doi:10.19678/j.issn.1000-3428.0048890

计算机工程 ›› 2019, Vol. 45 ›› Issue (3): 212-216,224. doi: 10.19678/j.issn.1000-3428.0048890

基于深度学习的图像美观度评价

费延佳,李福翠,邵枫

宁波大学信息与工程学院,浙江宁波 315211

收稿日期:2017-10-10 出版日期:2019-03-15 发布日期:2019-03-15
作者简介:费延佳(1992—),男,硕士研究生,主研方向为图像质量评价;李福翠,讲师;邵枫,教授。
基金资助:
国家自然科学基金(61622109)。

Image Aesthetic Assessment Based on Deep Learning

FEI Yanjia,LI Fucui,SHAO Feng

Department of Information Science and Engineering,Ningbo University,Ningbo,Zhejiang 315211,China

Received:2017-10-10 Online:2019-03-15 Published:2019-03-15

摘要/Abstract

摘要：

基于深度卷积神经网络的特征提取方法比传统手工特征提取方法更加贴近人类大脑的视觉感受。为此,建立一种两通道组合图像美观度评价模型。使用美学信息通道和场景信息通道的组合来自动提取图像中美学信息和场景类别信息,通过融合两类信息最终形成美感分类器。在AVA库上进行训练和测试,结果表明,与图像局部特征提取方法相比,该模型结构较简洁,且具有较高的分类准确率。

关键词: 美观度评价, 卷积神经网络, 场景识别, 两通道, 分类网络

Abstract:

The method of extracting features based on deep Convolutional Neural Network(CNN) is closer to the visual perception of the human brain than the traditional manual extraction feature method.Therefore,a two-channel combination model is proposed.The combination of the aesthetic information channel and the scene information channel is used to automatically extract the aesthetic information and scene category information in the image,and finally combine the two types of information to form an aesthetic classifier.The training and testing on the AVA library show that compared with the image local feature extraction method,the model structure is simple and has high classification accuracy.

Key words: aesthetic assessment, Convolutional Neural Network(CNN), scene recognition, two-channel, classification network

中图分类号:

TN911.73

费延佳,李福翠,邵枫. 基于深度学习的图像美观度评价[J]. 计算机工程, 2019, 45(3): 212-216,224.

FEI Yanjia,LI Fucui,SHAO Feng. Image Aesthetic Assessment Based on Deep Learning[J]. Computer Engineering, 2019, 45(3): 212-216,224.

http://www.ecice06.com/CN/Y2019/V45/I3/212

参考文献

［1］DONG Z,TIAN X M.Multi-level photo quality assessment with multi-view features［J］.Neurocomputing,2015,168:308-319.
［2］TONG H,LI M,ZHANG H J,et al.Classification of digital photos taken by photographers or home users［C］//Proceedings of Pacific-Rim Conference on Multimedia.Berlin,Germany:Springer,2004:198-205.
［3］DATTA R,JOSHI D,LI J,et al.Studying aesthetics in photographic images using a computational approach［C］//Proceedings of the 9th European Conference on Computer Vision.Berlin,Germany:Springer,2006:288-301.
［4］LOWE D G.Distinctive image features from scale-invariant keypoints［J］.International Journal of Computer Vision,2004,60(2):91-110.
［5］MARCHESOTTI L,PERRONNIN F,LARLUS D,et al.Assessing the aesthetic quality of photographs using generic image descriptors［C］//Proceedings of International Conference on Computer Vision.Washington D.C.,USA:IEEE Computer Society,2011:1784-1791.
［6］高寒.基于图像分类的图像美学评价研究［D］.哈尔滨:哈尔滨工业大学,2013.
［7］LU X,LIN Z,JIN H,et al.Rating image aesthetics using deep learning［J］.IEEE Transactions on Multimedia,2015,17(11):2021-2034.
［8］WANG Z,CHANG S,DOLCOS F,et al.Brain-inspired deep networks for image aesthetics assessment［EB/OL］.［2017-08-25］.https://arxiv.org/pdf/1601.04155.pdf.
［9］TIAN X,DONG Z,YANG K,et al.Query-dependent aesthetic model with deep learning for photo quality assessment［J］.IEEE Transactions on Multimedia,2015,17(11):2035-2048.
［10］ZHOU Y,LU X,ZHANG J,et al.Joint image and text representation for aesthetics analysis［C］//Proceedings of the 24th ACM International Conference on Multimedia.New York,USA:ACM Press,2016:262-266.
［11］POWER J D,COHEN A L,NELSON S M,et al.Functional network organization of the human brain［J］.Neuron,2011,72(4):665-678.
［12］VEERINA P.Learning good taste:classifying aesthetic images［EB/OL］.［2017-08-25］.http://101.110.118.21/cs231n.stanford.edu/reports/2015/pdfs/pveerina_final.pdf.
［13］MAI L,JIN H,LIU F.Composition-preserving deep photo aesthetics assessment［C］//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:497-506.
［14］KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks［C］//Proceedings of the 25th International Conference on Neural Information Processing Systems.［S.l.］:Curran Associates Inc.,2012:1097-1105.
［15］Dpchallenge［EB/OL］.［2017-08-25］.https://www.dpchallenge.com/.
［16］JIA Y,SHELHAMER E,DONAHUE J,et al.Caffe:convolutional architecture for fast feature embedding［EB/OL］.［2017-08-25］.https://arxiv.org/pdf/1408.5093.pdf.
［17］LUO Y,TANG X.Photo and video quality evaluation:focusing on the subject［C］//Proceedings of the 10th European Conference on Computer Vision.Berlin,Germany:Springer,2008:386-399.
［18］LO K Y,LIU K H,CHEN C S.Assessment of photo aesthetics with efficiency［C］//Proceedings of the 21st International Conference on Pattern Recognition.Washington D.C.,USA:IEEE Press,2012:2186-2189.
［19］WANG W,ZHAO M,WANG L,et al.A multi-scene deep learning model for image aesthetic evaluation［J］.Signal Processing:Image Communication,2016,47:511-518.
［20］KONG S,SHEN X,LIN Z,et al.Photo aesthetics ranking network with attributes and content adaptation［C］//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2016:662-679.
［21］王伟凝,王励,赵明权,等.基于并行深度卷积神经网络的图像美感分类［J］.自动化学报,2016,42(6):904-914.

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