Using fan-made content, subtitles and face recognition for character-centric video summarization

Harrando, Ismail; Reboud, Alison; Lisena, Pasquale; Troncy, Raphaël; Laaksonen, Jorma; Virkkunen, Anja; Kurimo, Mikko
TRECVID 2020, International Workshop on Video Retrieval Evaluation, 8-11 December 2020 (Virtual Conference)

This paper describes a fan-driven and character-centered approach proposed by the MeMAD team for the 2020 TRECVID [Awad et al. 2020] Video Summarization Task. In terms of data, besides the provided videos, scripts and master shot boundaries, our approach relies on fan-made content, more precisely on the BBC EastEnders episode synopses from its Fandom Wiki1. We also use BBC EastEnders characters’ images crawled from a search engine to train a face recognition system. All our runs use the same method, but with varying constraints of the number of shots and the maximum duration. The shots included in the summaries are the ones whose transcripts and visual content have the highest similarity with sentences from the synopsis. The runs submitted are as follows: • MeMAD1:5 shots with highest similarity scores and the total duration is


Type:
Conférence
Date:
2020-12-07
Department:
Data Science
Eurecom Ref:
6441
Copyright:
© NIST. Personal use of this material is permitted. The definitive version of this paper was published in TRECVID 2020, International Workshop on Video Retrieval Evaluation, 8-11 December 2020 (Virtual Conference) and is available at :

PERMALINK : https://www.eurecom.fr/publication/6441