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Carnegie Mellon Robotics Institute

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Abstract

The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Semi-supervised training is a means for reducing the effort needed to prepare the training set by training the model with a small number of fully labeled example and an additional set of unlabeled or weakly labeled examples. In this work we present a semi-supervised approach to training object detection systems based on self-training. We implement our approach as a wrapper around the training process of an existing detector and present empirical results. The key contributions of this empirical study is to demonstrate that a model trained in this manner can achieve results comparable to a model trained in the traditional manner using a much larger set of fully labeled data, and that a training data selection metric that is defined independently of the detector greatly outperforms a selection metric based on the confidence generated by the detector.

Notes

Text Reference

Charles Rosenberg, Martial Hebert, and Henry Schneiderman, "Semi-Supervised Self-Training of Object Detection Models," Seventh IEEE Workshop on Applications of Computer Vision, January, 2005.

BibTeX Reference

@inproceedings{Hebert_2005_4875,
   author = "Charles Rosenberg and Martial Hebert and Henry Schneiderman",
   title = "Semi-Supervised Self-Training of Object Detection Models",
   booktitle = "Seventh IEEE Workshop on Applications of Computer Vision",
   month = "January",
   year = "2005",
}

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