A Caltech Library Service

Object class recognition by unsupervised scale-invariant learning

Fergus, R. and Perona, P. and Zisserman, A. (2003) Object class recognition by unsupervised scale-invariant learning. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE , Los Alamitos, CA, pp. 264-271. ISBN 0769519008.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).

Item Type:Book Section
Related URLs:
Perona, P.0000-0002-7583-5809
Additional Information:© 2003 IEEE. Issue Date: 18-20 June 2003. Date of Current Version: 15 July 2003. Timor Kadir for advice on the feature detector. D. Roth for providing the Cars (Side) dataset. Funding was provided by CNSE, the UK EPSRC, and EC Project CogViSys.
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)UNSPECIFIED
Other Numbering System:
Other Numbering System NameOther Numbering System ID
INSPEC Accession Number7770148
Record Number:CaltechAUTHORS:20111018-135127518
Persistent URL:
Official Citation:Fergus, R.; Perona, P.; Zisserman, A.; , "Object class recognition by unsupervised scale-invariant learning," Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on , vol.2, no., pp. II-264- II-271 vol.2, 18-20 June 2003 doi: 10.1109/CVPR.2003.1211479 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27284
Deposited By: Tony Diaz
Deposited On:25 Oct 2011 15:13
Last Modified:09 Nov 2021 16:47

Repository Staff Only: item control page