  This paper presents a novel retrieval pipeline for video collections, which
aims to retrieve the most significant parts of an edited video for a given
query, and represent them with thumbnails which are at the same time
semantically meaningful and aesthetically remarkable. Videos are first
segmented into coherent and story-telling scenes, then a retrieval algorithm
based on deep learning is proposed to retrieve the most significant scenes for
a textual query. A ranking strategy based on deep features is finally used to
tackle the problem of visualizing the best thumbnail. Qualitative and
quantitative experiments are conducted on a collection of edited videos to
demonstrate the effectiveness of our approach.
