Objective keyword selection for lecture video annotation

Ali Shariq Imran, Laksmita Rahadianti, Faouzi Alaya Cheikh, Sule Yildirim Yayilgan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper presents an objective keyword selection method called visualness with Lesk disambiguation (VLD) for describing educational videos with semantic tags. It extends the work on automatically extracting and associating meaningful keywords carried out in 'semantic tags for lecture videos' for efficient indexing and retrieval. VLD uses lecture videos and surrogates documents such as lecture transcripts to extract potential candidate keywords. The candidate keywords undergo a series of selection process extracting fewer but more meaningful keywords based on word sense disambiguation (WSD) and visual similarity. The objective metric then selects top ranking keywords by employing a rank cut-off method. The proposed metric is validated by comparing the automatically selected keywords to those obtained manually, suggesting that the words selected by the proposed objective metric correlate highly with those selected by viewers. The results are further compared to traditional term frequency inverse document frequency (TF-IDF) and state-of-the-art latent Dirichlet allocation (LDA) method, with an improved accuracy of 68.18% on 30 lecture videos.

Original languageEnglish
Title of host publicationEUVIP 2014 - 5th European Workshop on Visual Information Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479945726
DOIs
Publication statusPublished - 22 Jan 2015
Event5th European Workshop on Visual Information Processing, EUVIP 2014 - Paris, France
Duration: 10 Dec 201412 Dec 2014

Publication series

NameEUVIP 2014 - 5th European Workshop on Visual Information Processing

Conference

Conference5th European Workshop on Visual Information Processing, EUVIP 2014
Country/TerritoryFrance
CityParis
Period10/12/1412/12/14

Keywords

  • objective metrics
  • semantic keyword selection
  • video annotation
  • visualness
  • word sense disambiguation

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