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

  • Christoph H. Lampert
  • Matthew B. Blaschko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)


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.


Object Detection Dependency Graph Average Precision Candidate Region Object Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christoph H. Lampert
    • 1
  • Matthew B. Blaschko
    • 1
  1. 1.Department for Empirical InferenceMax Planck Institute for Biological CyberneticsTübingenGermany

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