Home/A framework for learning to recognize and segment object classes using weakly supervised training data

A framework for learning to recognize and segment object classes using weakly supervised training data

Caroline Pantofaru and Martial Hebert
Conference Paper, Carnegie Mellon University, British Machine Vision Conference, September, 2007

Download Publication (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

The continual improvement of object recognition systems has resulted in an increased demand for their application to problems which require an exact pixel-level object segmentation. In this paper, we illustrate an example of an object class recognition and segmentation system which is trained using weakly supervised training data, with the goal of examining the influence that different model choices can have on its performance. In order to achieve pixel-level labeling for rigid and deformable objects, we employ regions generated by unsupervised segmentation as the spatial support for our image features, and explore model selection issues related to their representation. Numerical results for pixel-level accuracy are presented on two challenging and varied datasets.

BibTeX Reference
@conference{Pantofaru-2007-9811,
title = {A framework for learning to recognize and segment object classes using weakly supervised training data},
author = {Caroline Pantofaru and Martial Hebert},
booktitle = {British Machine Vision Conference},
keyword = {object recognition, object segmentation, weakly supervised},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2007},
address = {Pittsburgh, PA},
}
2017-09-13T10:42:02+00:00