  A significant proportion of individuals' daily activities is experienced
through digital devices. Smartphones in particular have become one of the
preferred interfaces for content consumption and social interaction.
Identifying the content embedded in frequently-captured smartphone screenshots
is thus a crucial prerequisite to studies of media behavior and health
intervention planning that analyze activity interplay and content switching
over time. Screenshot images can depict heterogeneous contents and
applications, making the a priori definition of adequate taxonomies a
cumbersome task, even for humans. Privacy protection of the sensitive data
captured on screens means the costs associated with manual annotation are
large, as the effort cannot be crowd-sourced. Thus, there is need to examine
utility of unsupervised and semi-supervised methods for digital screenshot
classification. This work introduces the implications of applying clustering on
large screenshot sets when only a limited amount of labels is available. In
this paper we develop a framework for combining K-Means clustering with Active
Learning for efficient leveraging of labeled and unlabeled samples, with the
goal of discovering latent classes and describing a large collection of
screenshot data. We tested whether SVM-embedded or XGBoost-embedded solutions
for class probability propagation provide for more well-formed cluster
configurations. Visual and textual vector representations of the screenshot
images are derived and combined to assess the relative contribution of
multi-modal features to the overall performance.
