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...

• Type: Conference
• Year: 2005
• Where: WACV
• Authors: Chuck Rosenberg, Martial Hebert, Henry Schneiderman
• Keywords: Appearance-based Object Detection, Object Detection Systems, Training, WACV 2005