CASIA OpenIR  > 智能感知与计算研究中心
Visual Aesthetic Quality Assessment with a Regression Model
Yueying Kao; Chong Wang; Kaiqi Huang
2015
Conference NameIEEE International Conference on Image Processing
Source PublicationProc. IEEE International Conference on Image Processing 2015
Pages1583-1587
Conference Date2015-09-01
Conference PlaceQuebec, Canada
AbstractAesthetic image analysis has drawn much attention in recent years. However, assessing the aesthetic quality especially aesthetic score prediction is a challenging problem. In this paper, we interpret aesthetic quality assessment as a regression problem and present a new framework by directly training a regression model using a neural network. Firstly, to extract the aesthetic features which are difficult to design manually, we utilize the convolutional network to learn the features. Then, a regression model is trained based on the aesthetic features. Different from classification models which can only predict aesthetic class (high or low) in most existing works, the regression model can predict continuous aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can assess the degree of aesthetic quality similar to human visual system effectively and outperforms the state-of-the-art methods.
KeywordAgriculture   feature Extraction   image Analysis   predictive Models   quality Assessment   visual Systems   visualization   aesthetic Image Analysis   convolutional Neural Network   regression
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12677
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Yueying Kao,Chong Wang,Kaiqi Huang. Visual Aesthetic Quality Assessment with a Regression Model[C],2015:1583-1587.
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