This paper provides an overview of the Predicting Media Interestingness task that is organized as part of the Media-Eval 2016 Benchmarking Initiative for Multimedia Evaluation. The task, which is running for the first year, expects participants to create systems that automatically select images and video segments that are considered to be the most interesting for a common viewer. In this paper, we present the task use case and challenges, the proposed data set and ground truth, the required participant runs and the evaluation metrics.
|Number of pages||3|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2016|
|MoE publication type||A4 Article in a conference publication|