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Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
Faculty of Electrical Engineering and Computing, University of Zagreb.ORCID iD: 0000-0002-4689-0956
Faculty of Electrical Engineering and Computing, University of Zagreb.
Faculty of Electrical Engineering and Computing, University of Zagreb.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6763-5487
2018 (English)In: Proceedings of BMVC 2018 and Workshops, Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition , 2018, p. 1-11, article id 896Conference paper, Published paper (Refereed)
Abstract [en]

Ren et al. [17] recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

Place, publisher, year, edition, pages
Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition , 2018. p. 1-11, article id 896
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-152550OAI: oai:DiVA.org:liu-152550DiVA, id: diva2:1261236
Conference
British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, UK, 3-6 September 2018
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2019-08-22Bibliographically approved

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Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment(512 kB)2 downloads
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File name FULLTEXT02.pdfFile size 512 kBChecksum SHA-512
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Markus, NenadAhlberg, Jörgen

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
  • rtf