Evaluating photo aesthetics using machine learning

Domen Pogačnik and Robert Ravnik and Narvika Bovcon and Franc Solina (2012) Evaluating photo aesthetics using machine learning. In: Data Mining and Data Warehouses (SiKDD 2012), Information Society - IS 2012, 8-12 October 2012, Ljubljana.

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Abstract

In this paper we propose a method for au- tomatic assessment of aesthetic appeal of pho- tographs. We identify significant parameters that distinguish high quality photography from low quality snapshots. On the basis of these parameters, we defined calculable features for automatic assessment of photography aesthet- ics using machine learning methods. The cal- culation of features depends heavily on the identification of the subject in photographs. With the subject identified, we defined and im- plemented various features to analyze various aspects of a photograph. The features were tested on two datasets. First dataset was ob- tained from Flickr and manually labeled for evaluation. Second dataset was based on pho- tographs from DPChallenge portal where sub- jects were identified with a face detection algo- rithm. Both experiments showed some promis- ing results. In this article we specify the fea- tures which contribute to a successful classifi- cation of photographs, analyze their influence and discuss the results. In conclusion, we o↵er some suggestions for further research.

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