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WACV
2005
IEEE

Semi-Supervised Self-Training of Object Detection Models

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Semi-Supervised Self-Training of Object Detection Models
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 examples 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 object 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 th...
Chuck Rosenberg, Martial Hebert, Henry Schneiderma
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where WACV
Authors Chuck Rosenberg, Martial Hebert, Henry Schneiderman
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