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Discriminative tree-based feature mapping
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2013 (English)In: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, British Machine Vision Association, BMVA , 2013Conference paper, Published paper (Refereed)
Abstract [en]

For object classification and detection, the algorithm pipeline often involves classifying feature vectors extracted from image patches. Existing features such as HOG, fail to map the image patches into a space where a linear hyperplane is suitable for separating the classes, while many non-linear classification methods are too expensive for many tasks. We propose a sparse tree-based mapping method that learns a mapping of the feature vector to a space where a linear hyperplane can better separate negative and positive examples. The learned mapping function Φ(x) results in significant improvement for image patch classification with HOG and LBP-features over other feature mapping methods on VOC2007 and INRIAPerson datasets.

Place, publisher, year, edition, pages
British Machine Vision Association, BMVA , 2013.
Keyword [en]
Classification (of information), Computer vision, Geometry, Feature mapping, Feature vectors, Image patches, Mapping functions, Mapping method, Nonlinear classification, Object classification, Positive examples, Mapping
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-147258DOI: 10.5244/C.27.71ISI: 000346352700068Scopus ID: 2-s2.0-84898404810OAI: oai:DiVA.org:kth-147258DiVA: diva2:729650
Conference
2013 24th British Machine Vision Conference, BMVC 2013; Bristol; United Kingdom; 9 September 2013 through 13 September 2013
Note

QC 20140626

Available from: 2014-06-26 Created: 2014-06-25 Last updated: 2015-10-06Bibliographically approved

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Citation style
  • apa
  • harvard1
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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