Abstract
Automatic aesthetic quality assessment is a computer vision problem in which we quantify the attractiveness or the appealingness of a photograph. This is especially useful in social networks, where the amount of images generated each day requires automation for processing. This work presents Aesthetic Selector, an application able to identify images of high aesthetic quality, showing also relevant information about the decisions and providing the use of the most appropriate filters to enhance a given image. We then analyzed the main proposals in the aesthetic quality field, describing their strengths and weaknesses in order to determine the filters to be included in the application Aesthetic Selector. This proposed application was tested, giving good results, in three different scenarios: image selection, image finding, and filter selection. Besides, we carried out a study of distinct visualization tools to better understand the models’ behavior. These techniques also allow detecting which areas are more relevant within the images when models perform classification. The application also includes this interpretability module. Aesthetic Selector is an innovative and original program, because in the field of aesthetic quality in photography, there are no applications that identify high-quality images and also because it offers the capability of showing information about which parts of the image have affected this decision.
Highlights
One of the most important and active fields in the scientific community is computer vision due to its large number of applications in different domains
Deep neural networks [1] have enabled solutions to computer vision problems that, until recently, seemed unapproachable. This scenario has caused more complex tasks to emerge such as the automatic assessment of aesthetic quality in photography
Continuous naive Bayes, support vector machines (SVMs), and extreme learning machines (ELMs) were trained with each set, and their results are reported in terms of accuracy, balanced accuracy, and the area under the curve value (AUC)
Summary
One of the most important and active fields in the scientific community is computer vision due to its large number of applications in different domains. Deep neural networks [1] have enabled solutions to computer vision problems that, until recently, seemed unapproachable. This scenario has caused more complex tasks to emerge such as the automatic assessment of aesthetic quality in photography. The concept of aesthetic quality in photography refers to those image properties that make pictures attractive or pleasant for most people. These properties include applied filters, the harmony of colors, etc. Notice that we should not confuse this aesthetic concept with the quality of an image in terms of resolution
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