Comparison of Generative and Discriminative Techniques for Object Detection and Classification

  • Ilkay Ulusoy
  • Christopher M. Bishop
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)


Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this chapter we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use these models to illustrate the relative merits of the two approaches in the context of a data set of widely varying images of non-rigid objects (animals). Our results support the assertion that neither approach alone will be sufficient for large scale object recognition, and we discuss techniques for combining the strengths of generative and discriminative approaches.


Principle Component Analysis Interest Point Segmented Data Discriminative Model Interest Point Detector 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ilkay Ulusoy
    • 1
  • Christopher M. Bishop
    • 2
  1. 1.Computer Vision and Intelligent Systems Research Lab.METUAnkaraTurkey
  2. 2.Microsoft ResearchCambridgeU.K.

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