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

  • Svetlana Lazebnik
  • Cordelia Schmid
  • Jean Ponce
Conference paper

DOI: 10.1007/11957959_22

Volume 4170 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Lazebnik S., Schmid C., Ponce J. (2006) A Discriminative Framework for Texture and Object Recognition Using Local Image Features. In: Ponce J., Hebert M., Schmid C., Zisserman A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg

Abstract

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.

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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