Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |

xie04slightly

Lexing Xie, Patrick Pérez. Slightly Supervised Learning of Part-Based Appearance Models. In IEEE Workshop on Learning in Computer Vision and Pattern Recognition, Washington D. C, June 2004.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

We extend a GMM-based approach for learning part-based appearance models of object categories, to the unsupervised case where positive examples are corrupted with clutter. To this end, we derive an original version of EM which is able to fit one GMM per class based on partially labeled data. We also allow ourselves a small fraction of un-corrupted positive examples, thus obtaining an effective, yet cheap, {\em slightly} supervised learning. Proposed technique allows as well a saliency-based ranking and selection of learnt mixture components. Experiments show that both the semi-supervised GMM fitting with side information and the component selection are effective in identifying salient patches in the appearance of a class of objects. They are thus promising tools to learn class-specific models and detectors similar to those by Weber {et al.}, but at a lower computational cost, while accommodating larger numbers of atomic parts

Contact

Lexing Xie

BibTex Reference

@InProceedings{xie04slightly,
   Author = {Xie, Lexing and Pérez, Patrick},
   Title = {Slightly Supervised Learning of Part-Based Appearance Models},
   BookTitle = {IEEE Workshop on Learning in Computer Vision and Pattern Recognition},
   Address = {Washington D. C},
   Month = {June},
   Year = {2004}
}

EndNote Reference [help]

Get EndNote Reference (.ref)

 
bar

For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).