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Towards fusion of collective knowledge and audio-visual content features for annotating broadcast video

Published: 16 April 2013 Publication History

Abstract

Broadcasters produce vast collections of video content. However, the lack of fine-grained annotations makes it difficult to retrieve video fragments of interest from these vast collections. Indeed, manual annotation of video content is labour-intensive and time-consuming. Moreover, the applicability of algorithms for automatic annotation of video content is limited, given that too many prerequisites need to be fulfilled and that a lot of concepts are unidentifiable. At the same time, people are using social media to share their thoughts about the content they view on television. Therefore, in this Ph.D. research, we plan to investigate novel machine learning-based approaches towards the task of fine-grained annotation of broadcast video content, fusing the collective knowledge present in social media with the output of audio-visual content analysis algorithms.

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  • (2022)Textual variations affect human judgements of sentiment valuesElectronic Commerce Research and Applications10.1016/j.elerap.2022.10114953:COnline publication date: 1-May-2022

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      cover image ACM Conferences
      ICMR '13: Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
      April 2013
      362 pages
      ISBN:9781450320337
      DOI:10.1145/2461466
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      Published: 16 April 2013

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      Author Tags

      1. annotation
      2. broadcast video
      3. collective knowledge
      4. content analysis
      5. multi-modal fusion
      6. signal processing
      7. social media

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      Overall Acceptance Rate 254 of 830 submissions, 31%

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      • (2022)Textual variations affect human judgements of sentiment valuesElectronic Commerce Research and Applications10.1016/j.elerap.2022.10114953:COnline publication date: 1-May-2022

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