DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task

This paper describes our approach for the submission to the Mediaeval 2017 Predicting Media Interestingness Task, which was particularly developed for the Image subtask. An approach using a late fusion strategy is employed, combining classifiers from different features by stacking them using logistic regression (LR). As the task ground truth was based on pairwise evaluation of shots or keyframe images within the same movie, next to using precomputed features as-is, we also include a more contextual feature, considering averaged feature values over each movie. Furthermore, we also consider evaluation outcomes for the heuristic algorithm that yielded the highest MAP score on the 2016 Image subtask. Considering results obtained for the development and test sets, our late fusion method shows consistent performance on the Image subtask, but not on the Video subtask. Furthermore, clear differences can be observed between MAP@10 and MAP scores.