PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

4.4. Summary 59
(a) scene 1 (b) scene 2 (c) improved results
Figure 4.12: A scene specific car detector for scene 1 (a) is applied on a “similar” scene
(b). The poor behavior can be significantly improved using unlabeled data taken from the
second scene as shown by a typical frame (c).
Figure 4.13: ROC curve for “scene 2” for the starting classifier (red) and the improved
classifier (blue) using unlabeled samples from the target scene.
sub-patches from the scene these 2000 serve as unlabeled examples to train a SemiBoost
classifier with 30 weak classifiers using the classifier trained on scene 1 as prior. A typical
frame superimposed with the detection result is shown in Figure 4.12(b)(c). The detection
results improved (much lower false positive rate and higher detection rate) as shown in
Figure 4.13.
4.4 Summary
In this chapter, we introduced a boosting algorithm that combines semi-supervised learning
with learning of distance metrics. Learning distance metrics is especially interesting
for computer vision applications because for a given task it is often hard to say which
metric to take. Distance functions can be learned using a small amount of data and can be
used as prior information in order to support the exploitation of unlabeled data during a
SSL process. We demonstrated the approach on several vision applications such face detection
and visual transfer learning. In the following chapter, we will show how to extend