Predicting Interestingness of Visual Content

The ability of multimedia data to attract and keep people’s interest for longer periods of time is gaining more and more importance in the fields of information retrieval and recommendation, especially in the context of the ever growing market value of social media and advertising. In this chapter we introduce a benchmarking framework (dataset and evaluation tools) designed specifically for assessing the performance of media interestingness prediction techniques. We release a dataset which consists of excerpts from 78 movie trailers of Hollywood-like movies. These data are annotated by human assessors according to their degree of interestingness. A real-world use scenario is targeted, namely interestingness is defined in the context of selecting visual content for illustrating a Video on Demand (VOD) website. We provide an in-depth analysis of the human aspects of this task, i.e., the correlation between perceptual characteristics of the content and the actual data, as well as of the machine aspects by overviewing the participating systems of the 2016 MediaEval Predicting Media Interestingness campaign. After discussing the state-of-art achievements, valuable insights, existing current capabilities as well as future challenges are presented.