Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on (2005)
Jan. 5, 2005 to Jan. 7, 2005
Chuck Rosenberg , Google, Inc., Mountain View, CA
Henry Schneiderman , Carnegie Mellon University, Pittsburgh, PA
Martial Hebert , Carnegie Mellon University, Pittsburgh, PA
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 the detector greatly outperforms a selection metric based on the detection confidence generated by the detector.
Chuck Rosenberg, Henry Schneiderman, Martial Hebert, "Semi-Supervised Self-Training of Object Detection Models", Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on, vol. 01, no. , pp. 29-36, 2005, doi:10.1109/ACVMOT.2005.107