Conference paper Open Access

Content Adaptation, Personalisation and Fine-grained Retrieval: Applying AI to Support Engagement with and Reuse of Archival Content at Scale

Rasa Bocyte; Johan Oomen


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{
  "description": "<p>Recent technological advances in the distribution of audiovisual content have opened up many opportunities for media archives to fulfil their outward-facing ambitions and easily reach large audiences with their content. This paper reports on the initial results of the ReTV research project that aims to develop novel approaches for the reuse of audiovisual collections. It addresses the reuse of archival collections from three perspectives: content holders (broadcasters and media archives) who want to adapt audiovisual content for distribution on social media, end-users who have switched from linear television to online platforms to consume audiovisual content and creatives in the media industry who seek audiovisual content that could be used in new productions. The paper presents three uses cases that demonstrate how AI-based video analysis technologies can facilitate these reuse scenarios through video content adaptation, personalisation and fine-grained retrieval.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0003-1254-8869", 
      "@type": "Person", 
      "name": "Rasa Bocyte"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-1750-6801", 
      "@type": "Person", 
      "name": "Johan Oomen"
    }
  ], 
  "headline": "Content Adaptation, Personalisation and Fine-grained Retrieval: Applying AI to Support Engagement with and Reuse of Archival Content at Scale", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-03-27", 
  "url": "https://zenodo.org/record/3732471", 
  "keywords": [
    "Reuse", 
    "Video Summarisation", 
    "Content Adaptation", 
    "Personalisation", 
    "Multimedia Annotation", 
    "Retrieval"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5220/0009188505060511", 
  "@id": "https://doi.org/10.5220/0009188505060511", 
  "@type": "ScholarlyArticle", 
  "name": "Content Adaptation, Personalisation and Fine-grained Retrieval: Applying AI to Support Engagement with and Reuse of Archival Content at Scale"
}
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