PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

4.3. Experiments on Visual Classification and Detection 57
(a) prior (b) trained (c) combined
Figure 4.10: Detection results of a face detector (a) which serves as prior to build a
SemiBoost classifier using additional unlabeled data. This classifier alone (b) has not the
power of delivering good results but the combination improves the result essentially.
as well as a better alignment of the detections) as shown in Figure 4.11. Note, the trained
classifier alone consists only of 30 weak classifiers which yields poor results when applied
on the image (Figure 4.10). Of course, when using more weak classifiers it will learn the
prior information as well. Additionally, Figure 4.11 shows representative examples which
were obtained by our approach. Note that the most related approach, i.e., [Levin et al.,
2003], fails on this task when only using single views and we hence omit the detailed
comparison.
Scene Adaption
In the next experiment, we want to test our approach on a typical task occurring often in
practice; i.e., scene adaptation or visual transfer learning. In particular, in multi-camera
surveillance scenarios instead of collecting huge amounts of labeled data in order to train
a general object detector one can collect data for specific camera views or scenarios and
train a classifier only on these. This has the advantage that the model complexity can be
reduced, which results in faster detectors, and that usually the accuracy can be increased
because the classifier has only to discriminate the target object versus one specific background.
However, as collecting and labeling the data for each camera is cost and time
intensive (e.g., consider networks with tens or hundreds of surveillance cameras), one is
interesting in reusing as much information as possible between the cameras for training.
In the following, we demonstrate that our approach is also useful for such a problem.
For this purpose, we trained a car detector for a specific scene (i.e., “scene 1” illustrated
in Figure 4.12(a)) using 1000 labeled samples. When applying this classifier on
a different scene with a similar view point, as expected, it performs significantly worse.
Hence, in order to adapt the detector to the different scene (i.e., “scene 2”, Figure 4.12(b)),
we apply a simple motion detector to get potential positive samples of scene given in
Figure 4.12(b). After collecting 1000 of them and additionally cropping 1000 random