Modeling image aesthetics through aesthetics-related attributes

Automatic assessment of image aesthetics is a challenging task for the computer vision community that has a wide range of applications. The most promising state-of-the-art approaches are based on deep learning methods that jointly predict aesthetics-related attributes and aesthetics score. In this article, we propose a method that learns the aesthetics score on the basis of the prediction of aesthetics-related attributes. To this end, we extract a multi-level spatially pooled (MLSP) features set from a pretrained ImageNet network and then these features are used to train a Multi Layer Perceptron (MLP) to predict image aesthetics-related attributes. A Support Vector Regression machine (SVR) is finally used to estimate the image aesthetics score starting from the aesthetics-related attributes. Experimental results on the ”Aesthetics with Attributes Database” (AADB) demonstrate the effectiveness of our approach that outperforms the state of the art of about 5.5% in terms of Spearman’s Rankorder Correlation Coefficient (SROCC).

Beauty matters: reducing bounce rate by aesthetics of experience product portal page

PurposeThis study aims to investigate the impact of experience product portal page aesthetics on bounce rate.Design/methodology/approachThis research collected data from an online shop selling original design furniture on Taobao.com. It employed deep learning algorithm and manual coding to operationalize image and text aesthetics.FindingsThe empirical results indicate that text aesthetics has a U-shaped relationship with bounce rate, whereas the relationship between image aesthetics and bounce rate is insignificant. Moreover, the U-shaped relationship between text aesthetics and bounce rate is weakened by image aesthetics.Originality/valueThis study addresses an important but understudied topic – the bounce rate of experience products in the context of e-commerce. Although the high bounce rate has increasingly gained attention from practitioners, there remains a scarcity of research that addresses the effect of product portal page aesthetics in the specific context of experience products. The authors theorize product portal page aesthetics as the design elements of an e-commerce website and deeply analyzed the role of product portal page aesthetics by classifying it into text aesthetics and image aesthetics. The authors’ findings provide implications for online sellers and platforms to effectively design product profile pages to reduce the bounce rate.

A Genetic Algorithm to Combine Deep Features for the Aesthetic Assessment of Images Containing Faces

The automatic assessment of the aesthetic quality of a photo is a challenging and extensively studied problem. Most of the existing works focus on the aesthetic quality assessment of photos regardless of the depicted subject and mainly use features extracted from the entire image. It has been observed that the performance of generic content aesthetic assessment methods significantly decreases when it comes to images depicting faces. This paper introduces a method for evaluating the aesthetic quality of images with faces by encoding both the properties of the entire image and specific aspects of the face. Three different convolutional neural networks are exploited to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e., low/high, and continuous aesthetic score prediction on four different image databases in the state-of-the-art.