Deep Pairwise Classification and Ranking for Predicting Media Interestingness

With the explosive increase in the consumption of multimedia content in recent years, the field of media interestingness analysis has gained a lot of attention. This paper tackles the problem of image interestingness in videos and proposes a novel algorithm based on pairwise-comparisons of frames to rank all frames in a video. Experiments performed on the Predicting Media Interestingness dataset, affirm its effectiveness over existing solutions. In terms of the official metric i.e. Mean Average Precision at 10, it outperforms the previous state-of-the-art (to the best of our knowledge) on this dataset. Additional results on video interestingness substantiate the flexibility and performance reliability of our approach.