Content description for Predicting image Interestingness

In this article we analyze the prediction of image interestingness, a domain that is gaining importance in the fields such as recommendation systems, social media and advertising. We investigate the contribution of early and late fusion techniques, while using a set of image descriptors and analyze the best combinations that predict interestingness. Experimental validation is carried out on the MediaEval 2016 Predicting Media Interestingness image dataset. Results show the benefit of the introduction of late fusion approaches to solve the task, allowing to achieve better results than the state of the art.