Chapter

Toward Category-Level Object Recognition

Volume 4170 of the series Lecture Notes in Computer Science pp 423-442

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

  • Svetlana LazebnikAffiliated withCarnegie Mellon UniversityBeckman Institute, University of Illinois
  • , Cordelia SchmidAffiliated withCarnegie Mellon UniversityINRIA Rhône-Alpes
  • , Jean PonceAffiliated withCarnegie Mellon UniversityBeckman Institute, University of Illinois

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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.