Hierarchical Aesthetic Quality Assessment Using Deep Convolutional Neural Networks

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	Hierarchical Aesthetic Quality Assessment Using Deep Convolutional Neural Networks
	Kao, Yueying1 ; Huang, Kaiqi1 ; Maybank, Steve 2
刊名	Signal Processing: Image Communication
	2016
期号	47 页码:500-510
关键词	Aesthetic Image Analysis Convolutional Neural Networks Scene Object Texture
英文摘要	Aesthetic image analysis has attracted much attention in recent years. However, assessing the aesthetic quality and assigning an aesthetic score are challenging problems. In this paper, we propose a novel framework for assessing the aesthetic quality of images. Firstly, we divide the images into three categories: “scene”, “object” and “texture”. Each category has an associated convolutional neural network (CNN) which learns the aesthetic features for the category in question. The object CNN is trained using the whole images and a salient region in each image. The texture CNN is trained using small regions in the original images. Furthermore, an A&C CNN is developed to simultaneously assess the aesthetic quality and identify the category for overall images. For each CNN, classification and regression models are developed separately to predict aesthetic class (high or low) and to assign an aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can outperform the state-of-the-art methods for each category.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/14658]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Huang, Kaiqi
作者单位	1.CRIPAC&NLPR,Institute of Automation, Chinese Academy of Sciences,University of Chinese Academy of Sciences,Beijing,China 2.Department of Computer Science and Information Systems,Birkbeck College,University of London,London,UK
推荐引用方式 GB/T 7714	Kao, Yueying,Huang, Kaiqi,Maybank, Steve. Hierarchical Aesthetic Quality Assessment Using Deep Convolutional Neural Networks[J]. Signal Processing: Image Communication,2016(47):500-510.
APA	Kao, Yueying,Huang, Kaiqi,&Maybank, Steve.(2016).Hierarchical Aesthetic Quality Assessment Using Deep Convolutional Neural Networks.Signal Processing: Image Communication(47),500-510.
MLA	Kao, Yueying,et al."Hierarchical Aesthetic Quality Assessment Using Deep Convolutional Neural Networks".Signal Processing: Image Communication .47(2016):500-510.