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Integrating representative and discriminant models for object category detection
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2005 (English)In: Tenth IEEE International Conference on  (Volume:2 ) Computer Vision, 2005. ICCV 2005, IEEE Computer Society, 2005, 1363-1370 p.Conference paper, Published paper (Refereed)
Abstract [en]

Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative classifier. For each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. Given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. Then, the discriminative part verifies each hypothesis on the same codebook activations. The new algorithm exploits the strengths of both original methods, minimizing their weaknesses. Experiments on several databases show that our new approach performs better than its building blocks taken separately. Moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results.

Place, publisher, year, edition, pages
IEEE Computer Society, 2005. 1363-1370 p.
Series
IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499 ; II
Keyword [en]
Algorithms, Classification (of information), Codes (symbols), Database systems, Vocabulary control, Voice/data communication systems, Categorization algorithms, Codebook activations, Discriminative classifiers, Generative models, Object recognition
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-156548DOI: 10.1109/ICCV.2005.124ISI: 000233155100179Scopus ID: 2-s2.0-33745911319ISBN: 076952334X (print)ISBN: 978-076952334-7 OAI: oai:DiVA.org:kth-156548DiVA: diva2:767599
Conference
Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005, 17 October 2005 through 20 October 2005, Beijing, China
Note

QC 20141202

Available from: 2014-12-02 Created: 2014-12-01 Last updated: 2014-12-02Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
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
  • html
  • text
  • asciidoc
  • rtf