PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A sparse object category model for efficient learning and exhaustive recognition
Robert Fergus, Pietro Perona and Andrew Zisserman
In: CVPR 2005, 22-24 June 2005, San Diego, USA.

This is the latest version of this eprint.


We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse representation of the object, and consists of a star topology configuration of parts modeling the output of a variety of feature detectors. The optimal choice of feature types (whose repertoire includes interest points, curves and regions) is made automatically. In recognition, the model may be applied efficiently in an exhaustive manner, bypassing the need for feature detectors, to give the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers both successful classification and localization of the object within the image.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1132
Deposited By:Mudigonda Pawan Kumar
Deposited On:20 October 2005

Available Versions of this Item