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  A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection

Lampert, C., & Blaschko, M. (2008). A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection. In G. Rigoll (Ed.), Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008 (pp. 31-40). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C8EB-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-4306-B
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-69321-5_4.pdf (Publisher version)
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 Creators:
Lampert, C1, 2, Author           
Blaschko, MB1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Most current methods for multi-class object classification
and localization work as independent 1-vs-rest classifiers. They decide
whether and where an object is visible in an image purely on a per-class
basis. Joint learning of more than one object class would generally be
preferable, since this would allow the use of contextual information such
as co-occurrence between classes. However, this approach is usually not
employed because of its computational cost.
In this paper we propose a method to combine the efficiency of single
class localization with a subsequent decision process that works jointly
for all given object classes. By following a multiple kernel learning (MKL)
approach, we automatically obtain a sparse dependency graph of relevant
object classes on which to base the decision. Experiments on the
PASCAL VOC 2006 and 2007 datasets show that the subsequent joint
decision step clearly improves the accuracy compared to single class
detection.

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 Dates: 2008-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-69321-5_4
BibTex Citekey: 5259
 Degree: -

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Title: 30th Annual Symposium of the German Association for Pattern Recognition
Place of Event: München, Germany
Start-/End Date: 2008-06-10 - 2008-06-13

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Title: Pattern Recognition: 30th DAGM Symposium Munich, Germany, June 10-13, 2008
Source Genre: Proceedings
 Creator(s):
Rigoll, G, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 31 - 40 Identifier: ISBN: 978-3-540-69320-8

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 5096 Sequence Number: - Start / End Page: - Identifier: -