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Evaluating photo aesthetics using machine learning

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  • Computer And Information Science
  • Computer Science
  • Design
  • Philosophy


The purpose of this thesis is to identify the characteristics that influence the aesthetic appeal of photographs and to convert these characteristics into calculable features. Primarily, we examined the relation between the foreground object - subject and the photograph as a whole. The subject was manually identified with the assistance of experienced photographers. During our research, we implemented 31 features to analyze various aspects of a photograph, such as the color scheme, composition and proportions. A simple web application for labeling and the aesthetic assessment of photographs, which was developed as a part of our thesis, was used by experienced photographers in order to provide learning samples. On the basis of calculated features and gathered learning data, we used machine learning algorithms to create a model which is able to distinguish high quality/professional from low quality/snapshot photographs. We achieved 93 percent classification accuracy using SVM classifier. The features used by machine learning algorithm were analyzed with reliefF metric and the nomogram data provided by the Naive Bayes classifier. In the final part, we presented and discussed the influence of calculated features and suggested some guidelines for further research on the subject.

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