eprintid: 10126932
rev_number: 12
eprint_status: archive
userid: 608
dir: disk0/10/12/69/32
datestamp: 2021-04-30 14:29:18
lastmod: 2021-04-30 14:29:18
status_changed: 2021-04-30 14:29:18
type: proceedings_section
metadata_visibility: show
creators_name: Nazir, S
creators_name: Cagali, T
creators_name: Sadrzadeh, M
creators_name: Newell, C
title: Audiovisual, Genre, Neural and Topical Textual Embeddings for TV Programme Content Representation
ispublished: pub
divisions: UCL
divisions: A01
divisions: B04
divisions: C05
divisions: F48
keywords: Multimedia systems; Information filtering; Recommender systems; Content-based retrieval
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: TV programmes have their contents described by multiple means: textual subtitles, audiovisual files, and metadata such as genres. In order to represent these contents, we develop vectorial representations for their low-level multimodal features, group them with simple clustering techniques, and combine them using middle and late fusion. For textual features, we use LSI and Doc2Vec neural embeddings; for audio, MFCC's and Bags of Audio Words; for visual, SIFT, and Bags of Visual Words. We apply our model to a dataset of BBC TV programmes and use a standard recommender and pairwise similarity matrices of content vectors to estimate viewers' behaviours. The late fusion of genre, audio and video vectors with both of the textual embeddings significantly increase the precision and diversity of the results.
date: 2021-01-22
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ISM.2020.00041
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1854549
doi: 10.1109/ISM.2020.00041
isbn_13: 9781728186979
lyricists_name: Sadrzadeh, Mehrnoosh
lyricists_id: MSADR73
actors_name: Sadrzadeh, Mehrnoosh
actors_id: MSADR73
actors_role: owner
full_text_status: public
publication: Proceedings - 2020 IEEE International Symposium on Multimedia, ISM 2020
place_of_pub: Naples, Italy
pagerange: 197-200
event_title: 2020 IEEE International Symposium on Multimedia (ISM)
book_title: 020 IEEE International Symposium on Multimedia (ISM)
citation:        Nazir, S;    Cagali, T;    Sadrzadeh, M;    Newell, C;      (2021)    Audiovisual, Genre, Neural and Topical Textual Embeddings for TV Programme Content Representation.                     In:  020 IEEE International Symposium on Multimedia (ISM).  (pp. pp. 197-200).  IEEE: Naples, Italy.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10126932/1/SabaNazir.pdf