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Machine Learning for Rapid Image Classification
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis project techniques for training a rapid image classifier that can recognize an object of a predefined type has been studied. Classifiers have been trained with the AdaBoost algorithm, with and without the use of Viola-Jones cascades. The use of Weight trimming in the classifier training has been evaluated and resulted in a significant speed up of the training, as well as improving the performance of the trained classifier. Different preprocessings of the images have also been tested, but resulted for the most part in worse performance for the classifiers when used individually. Several rectangle shaped Haar-like features including novel versions have been evaluated and the magnitude versions proved to be best at separating the image classes.

Place, publisher, year, edition, pages
2013. , 66 p.
Keyword [en]
Image Classification, Machine Learning, Computer Vision, AdaBoost, Viola-Jones, Weight Trimming, Haar-like features
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-97375ISRN: LiTH-IMT/MI30-A-EX--13/512--SEOAI: oai:DiVA.org:liu-97375DiVA: diva2:647269
External cooperation
Saab
Subject / course
Medical Informatics
Presentation
2013-08-29, IMT1, Ingång 65, Campus US, Linköping, 10:15 (Swedish)
Supervisors
Examiners
Available from: 2013-09-12 Created: 2013-09-11 Last updated: 2013-09-12Bibliographically approved

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fulltext(1352 kB)350 downloads
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Niemi, Mikael
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
  • html
  • text
  • asciidoc
  • rtf