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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.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2013 (English)In: ICPRAM 2013: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 2013, 188-193 p.Conference paper, Published paper (Refereed)
Abstract [en]

Creating a single feature descriptors from a collection of feature responses is an often occurring task. As such the bag-of-words descriptors have been very successful and applied to data from a large range of different domains. Central to this approach is making an association of features to words. In this paper we present a new and novel approach to feature to word association problem. The proposed method creates a more robust representation when data is noisy and requires less words compared to the traditional methods while retaining similar performance. We experimentally evaluate the method on a challenging image classification data-set and show significant improvement to the state of the art.

Place, publisher, year, edition, pages
2013. 188-193 p.
Keyword [en]
Bag-of-words model, Image classification, Bag of words, Bag-of-words models, Descriptors, Different domains, Feature descriptors, State of the art, Word association, Information retrieval, Pattern recognition
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-134456Scopus ID: 2-s2.0-84877972429ISBN: 9789898565419 (print)OAI: oai:DiVA.org:kth-134456DiVA: diva2:675627
Conference
2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013, 15 February 2013 through 18 February 2013, Barcelona
Note

QC 20131204

Available from: 2013-12-04 Created: 2013-11-25 Last updated: 2013-12-04Bibliographically approved

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Maboudi Afkham, HeydarEk, Carl HenrikCarlsson, Stefan
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CiteExportLink to record
Permanent link

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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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  • text
  • asciidoc
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