Learning Signs From Subtitles: A Weakly Supervised Approach To Sign Language Recognition (bibtex)
by Helen Cooper, Richard Bowden
Abstract:
This paper introduces a fully-automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Based on head and hand tracking, a novel temporally constrained adaptation of apriori mining is used to extract similar regions of video, with the aid of a proposed contextual negative selection method. These regions are refined in the temporal domain to isolate the occurrences of similar signs in each example. The system is shown to automatically identify and segment signs from standard news broadcasts containing a variety of topics.
Reference:
Helen Cooper, Richard Bowden, "Learning Signs From Subtitles: A Weakly Supervised Approach To Sign Language Recognition", In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 2568 - 2574, 2009. (New Scientist Article on this work.)
Bibtex Entry:
@INPROCEEDINGS{Cooper_Learning_2009,
  author = {Helen Cooper and Richard Bowden},
  title = {Learning Signs From Subtitles: A Weakly Supervised Approach To Sign
	Language Recognition},
  booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision
	and Pattern Recognition},
  year = {2009},
  pages = {2568 -- 2574},
  address = {Miami, FL, USA},
  month = jun # { 20 -- 26},
  abstract = {This paper introduces a fully-automated, unsupervised method to recognise
	sign from subtitles. It does this by using data mining to align correspondences
	in sections of videos. Based on head and hand tracking, a novel temporally
	constrained adaptation of apriori mining is used to extract similar
	regions of video, with the aid of a proposed contextual negative
	selection method. These regions are refined in the temporal domain
	to isolate the occurrences of similar signs in each example. The
	system is shown to automatically identify and segment signs from
	standard news broadcasts containing a variety of topics.},
  comment = {<a target="_blank" href="http://www.newscientist.com/article/dn17431-computer-learns-sign-language-by-watching-tv.html">New
	Scientist Article on this work.</a>},
  doi = {10.1109/CVPRW.2009.5206647},
  url = {http://personal.ee.surrey.ac.uk/Personal/H.Cooper/research/papers/LearningSignFromSubtitles.pdf}
}
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