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.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Audiovisual, Genre, Neural and Topical Textual Embeddings for TV Programme Content Representation |
| Event: | 2020 IEEE International Symposium on Multimedia (ISM) |
| ISBN-13: | 9781728186979 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISM.2020.00041 |
| Publisher version: | https://doi.org/10.1109/ISM.2020.00041 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Multimedia systems; Information filtering; Recommender systems; Content-based retrieval |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10126932 |
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