TY - GEN
T1 - Objective keyword selection for lecture video annotation
AU - Imran, Ali Shariq
AU - Rahadianti, Laksmita
AU - Cheikh, Faouzi Alaya
AU - Yayilgan, Sule Yildirim
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/22
Y1 - 2015/1/22
N2 - 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.
AB - 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.
KW - objective metrics
KW - semantic keyword selection
KW - video annotation
KW - visualness
KW - word sense disambiguation
UR - http://www.scopus.com/inward/record.url?scp=84923536253&partnerID=8YFLogxK
U2 - 10.1109/EUVIP.2014.7018378
DO - 10.1109/EUVIP.2014.7018378
M3 - Conference contribution
AN - SCOPUS:84923536253
T3 - EUVIP 2014 - 5th European Workshop on Visual Information Processing
BT - EUVIP 2014 - 5th European Workshop on Visual Information Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th European Workshop on Visual Information Processing, EUVIP 2014
Y2 - 10 December 2014 through 12 December 2014
ER -