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Robust matching and recognition using context-dependent kernels

Published: 05 July 2008 Publication History

Abstract

The success of kernel methods including support vector machines (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics.
We focus in this paper on object recognition using a new type of kernel referred to as "context-dependent". Objects, seen as constellations of local features (interest points, regions, etc.), are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching, (2) a neighborhood criterion which captures the object geometry and (3) a regularization term. We will show that the fixed-point of this energy is a "context-dependent" kernel ("CDK") which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with "context-free" kernels.

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Cited By

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  • (2014)BibliographySemantic Multimedia Analysis and Processing10.1201/b17080-21(421-512)Online publication date: 18-Jun-2014
  • (2012)Multi-layer local graph words for object recognitionProceedings of the 18th international conference on Advances in Multimedia Modeling10.1007/978-3-642-27355-1_6(29-39)Online publication date: 4-Jan-2012

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cover image ACM Other conferences
ICML '08: Proceedings of the 25th international conference on Machine learning
July 2008
1310 pages
ISBN:9781605582054
DOI:10.1145/1390156
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Pascal
  • University of Helsinki
  • Xerox
  • Federation of Finnish Learned Societies
  • Google Inc.
  • NSF
  • Machine Learning Journal/Springer
  • Microsoft Research: Microsoft Research
  • Intel: Intel
  • Yahoo!
  • Helsinki Institute for Information Technology
  • IBM: IBM

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Association for Computing Machinery

New York, NY, United States

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Published: 05 July 2008

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Cited By

View all
  • (2014)BibliographySemantic Multimedia Analysis and Processing10.1201/b17080-21(421-512)Online publication date: 18-Jun-2014
  • (2012)Multi-layer local graph words for object recognitionProceedings of the 18th international conference on Advances in Multimedia Modeling10.1007/978-3-642-27355-1_6(29-39)Online publication date: 4-Jan-2012

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