PhD Thesis Semi-Supervised Ensemble Methods for Computer Vision

10.2. Outlook 141
e.g., may occur if one wants to improve a scene-specific car detector with unlabeled data
– probably from different scenarios – that does not contain any cars. For these situations,
the question arises if we can design methods that also benefit from unlabeled data that
does not contain the target objects. Also falling into this strand of research are recent
attempts, e.g., [Lampert et al., 2009, Farhadi et al., 2009, Wang et al., 2010] to describe
objects by their attributes and then re-use these attributes to also learn better classifiers
from categories where there is only a limited number of samples available.
Future work should probably also concentrate on incorporating special constraints
into SSL that are provided by computer vision applications. For instance, as we have
seen in the previous chapter, space-time regularization can be such a constraint. Another
constraint could be 3-dimensional information or, in general, multi-sensor fusion, etc..
The two main methods we investigated in this thesis were boosting and random forests.
Both methods have their individual advantages and disadvantages. Hence, in future work
we will concentrate on finding an algorithm that combines the two algorithms.