Scene-driven Retrieval In Edited Videos Using Aesthetic And Semantic Deep Features
Lorenzo Baraldi, Costantino Grana, Rita Cucchiara . Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval 2016 – 7 citations
[Paper]
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.