Deep Multimodal Features for Movie Genre and Interestingness Prediction

In this paper, we propose a multimodal framework for video segment interestingness prediction based on the genre and affective impact of movie content. We hypothesize that the emotional characteristic and impact of a video infer its genre, which can in turn be a factor for identifying the perceived interestingness of a particular video segment (shot) within the entire media. Our proposed approach is based on audio-visual deep features for perceptual content analysis. The multimodal content is quantified in a mid-level representation which consists in describing each audio-visual segment as a distribution over various genres (action, drama, horror, romance, sci-fi for now). Some segment might be more characteristic of a media and therefore be more interesting than a segment containing content with a neutral genre. Having determined the genre of individual video segments, we trained a classifier to produce an interestingness factor which is then used to rank segments. We evaluate our approach on the MediaEval2017 Media Interestingness Prediction Task Dataset (PMIT). We demonstrate that our approach outperforms the existing video interestingness approaches on the PMIT dataset in terms of Mean Average Precision.