  Wearable cameras stand out as one of the most promising devices for the
upcoming years, and as a consequence, the demand of computer algorithms to
automatically understand the videos recorded with them is increasing quickly.
An automatic understanding of these videos is not an easy task, and its mobile
nature implies important challenges to be faced, such as the changing light
conditions and the unrestricted locations recorded. This paper proposes an
unsupervised strategy based on global features and manifold learning to endow
wearable cameras with contextual information regarding the light conditions and
the location captured. Results show that non-linear manifold methods can
capture contextual patterns from global features without compromising large
computational resources. The proposed strategy is used, as an application case,
as a switching mechanism to improve the hand-detection problem in egocentric
videos.
