Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published 2005 | public
Book Section - Chapter

A Discriminative Framework for Modelling Object Classes

Abstract

Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn models from a set of training examples. Generative models learn their representations by considering data from a single class. Generative models are popular in computer vision for many reasons, including their ability to elegantly incorporate prior knowledge and to handle correspondences between object parts and detected features. However, generative models are often inferior to discriminative models during classification tasks. We study a discriminative approach to learning object categories which maintains the representational power of generative learning, but trains the generative models in a discriminative manner. The discriminatively trained models perform better during classification tasks as a result of selecting discriminative sets of features. We conclude by proposing a multiclass object recognition system which initially trains object classes in a generative manner, identifies subsets of similar classes with high confusion, and finally trains models for these subsets in a discriminative manner to realize gains in classification performance.

Additional Information

© 2005 IEEE. Issue Date: 20-25 June 2005. Date of Current Version: 25 July 2005. C. Rasmussen for making the conjugate gradient code 'minimize' publicly available and L. Zelnik, R. Fergus, F.F. Li, and G. Hinton for useful discussions.

Additional details

Created:
August 19, 2023
Modified:
January 13, 2024