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Texture Classification with Minimal Training Images
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2008 (English)In: 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, 717-720 p.Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this work is classifying texture from a single image under unknown lighting conditions. The current and successful approach to this task is to treat it as a statistical learning problem and learn a classifier from a set of training images, but this requires a sufficient number and variety of training images. We show that the number of training images required can be drastically reduced (to as few as three) by synthesizing additional training data using photometric stereo. We demonstrate the method on the PhoTex and ALOT texture databases. Despite the limitations of photometric stereo, the resulting classification performance surpasses the state of the art results.

Place, publisher, year, edition, pages
2008. 717-720 p.
Series
INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, ISSN 1051-4651
Keyword [en]
Classification performance, Lighting conditions, Minimal training, Photometric stereo, Single images, State of the art, Statistical learning, Texture classification, Training data, Training image, Face recognition, Photometry
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-31230DOI: 10.1109/ICPR.2008.4761388ISI: 000264729000176Scopus ID: 2-s2.0-77957936844ISBN: 978-1-4244-2174-9 (print)OAI: oai:DiVA.org:kth-31230DiVA: diva2:405911
Conference
19th International Conference on Pattern Recognition (ICPR 2008), Tampa, FL, DEC 08-11, 2008
Note
QC 20110324Available from: 2011-03-24 Created: 2011-03-11 Last updated: 2011-03-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • 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
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  • asciidoc
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