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Hierarchical Aesthetic Quality Assessment Using Deep Convolutional Neural Networks
Kao, Yueying1; Huang, Kaiqi1; Maybank, Steve2
Source PublicationSignal Processing: Image Communication
2016
Issue47Pages:500-510
AbstractAesthetic 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.
KeywordAesthetic Image Analysis Convolutional Neural Networks Scene Object Texture
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14658
Collection智能感知与计算研究中心
Corresponding AuthorHuang, Kaiqi
Affiliation1.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
Recommended Citation
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.
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