Validation of an Aesthetic Assessment System for Commercial Tasks

Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.

Multi-Output Learning Based on Multimodal GCN and Co-Attention for Image Aesthetics and Emotion Analysis

With the development of social networks and intelligent terminals, it is becoming more convenient to share and acquire images. The massive growth of the number of social images makes people have higher demands for automatic image processing, especially in the aesthetic and emotional perspective. Both aesthetics assessment and emotion recognition require a higher ability for the computer to simulate high-level visual perception understanding, which belongs to the field of image processing and pattern recognition. However, existing methods often ignore the prior knowledge of images and intrinsic relationships between aesthetic and emotional perspectives. Recently, machine learning and deep learning have become powerful methods for researchers to solve mathematical problems in computing, such as image processing and pattern recognition. Both images and abstract concepts can be converted into numerical matrices and then establish the mapping relations using mathematics on computers. In this work, we propose an end-to-end multi-output deep learning model based on multimodal Graph Convolutional Network (GCN) and co-attention for aesthetic and emotion conjoint analysis. In our model, a stacked multimodal GCN network is proposed to encode the features under the guidance of the correlation matrix, and a co-attention module is designed to help the aesthetics and emotion feature representation learn from each other interactively. Experimental results indicate that our proposed model achieves competitive performance on the IAE dataset. Progressive results on the AVA and ArtPhoto datasets also prove the generalization ability of our model.