|Visual Aesthetic Quality Assessment with a Regression Model|
|Yueying Kao; Chong Wang; Kaiqi Huang
|Conference Name||IEEE International Conference on Image Processing
|Source Publication||Proc. IEEE International Conference on Image Processing 2015
|Conference Place||Quebec, Canada
|Abstract||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.|
aesthetic Image Analysis
convolutional Neural Network
|Corresponding Author||Kaiqi Huang|
Yueying Kao,Chong Wang,Kaiqi Huang. Visual Aesthetic Quality Assessment with a Regression Model[C],2015:1583-1587.
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