A System for Video Recommendation Using Visual Saliency, Crowdsourced and Automatic Annotations

In this paper we present a system for content-based video recommendation that exploits visual saliency to better represent video features and content. Visual saliency is used to select relevant frames to be presented in a web-based interface to tag and annotate video frames in a social network; it is also employed to summarize video content to create a more effective video representation used in the recommender system. The system exploits automatic annotations from CNN-based classifiers on salient frames and user generated annotations. We evaluate several baseline approaches and show how the proposed method improves over them.