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A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues

  • Peter Carbonetto
  • Gyuri Dorkó
  • Cordelia Schmid
  • Hendrik Kück
  • Nando de Freitas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)

Abstract

This chapter presents a principled way of formulating models for automatic local feature selection in object class recognition when there is little supervised data. Moreover, it discusses how one could formulate sensible spatial image context models using a conditional random field for integrating local features and segmentation cues (superpixels). By adopting sparse kernel methods and Bayesian model selection and data association, the proposed model identifies the most relevant sets of local features for recognizing object classes, achieves performance comparable to the fully supervised setting, and consistently outperforms existing methods for image classification.

Keywords

Markov Chain Monte Carlo Object Class Scale Invariant Feature Transform Data Association Equal Error Rate 
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 2006

Authors and Affiliations

  • Peter Carbonetto
    • 1
  • Gyuri Dorkó
    • 2
  • Cordelia Schmid
    • 2
  • Hendrik Kück
    • 1
  • Nando de Freitas
    • 1
  1. 1.University of British ColumbiaVancouverCanada
  2. 2.INRIA Rhône-AlpesGrenobleFrance

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