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. HighlightsPropose an aesthetic quality assessment framework by dividing images into 3 categories.Three specific CNNs and an A&C CNN are constructed for aesthetic features learning.Different models for each CNN are developed to predict aesthetic class and score.
['Yueying Kao', 'Kaiqi Huang', 'Steve J. Maybank']
Hierarchical aesthetic quality assessment using deep convolutional neural networks