A Discriminative Framework for Texture and Object Recognition Using Local Image Features

  • Svetlana Lazebnik
  • Cordelia Schmid
  • Jean Ponce
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)


This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative classifier. Textures are represented using a visual dictionary found by quantizing appearance-based descriptors of local features. Object classes are represented using a dictionary of composite semi-local parts, or groups of nearby features with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.


Object Recognition Training Image Exponential Model Sift Descriptor Texture Recognition 
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

  • Svetlana Lazebnik
    • 1
  • Cordelia Schmid
    • 2
  • Jean Ponce
    • 1
  1. 1.Beckman InstituteUniversity of IllinoisUrbanaUSA
  2. 2.INRIA Rhône-AlpesMontbonnotFrance

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