Visual Aesthetic Quality Assessment with a Regression Model

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	Visual Aesthetic Quality Assessment with a Regression Model
	Yueying Kao; Chong Wang; Kaiqi Huang
	2015
会议日期	2015-09-01
会议地点	Quebec, Canada
关键词	Agriculture feature Extraction image Analysis predictive Models quality Assessment visual Systems visualization aesthetic Image Analysis convolutional Neural Network regression
页码	1583-1587
英文摘要	Aesthetic 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.
会议录	Proc. IEEE International Conference on Image Processing 2015
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12677]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yueying Kao,Chong Wang,Kaiqi Huang. Visual Aesthetic Quality Assessment with a Regression Model[C]. 见:. Quebec, Canada. 2015-09-01.