## Discovery of Social Relationships in Consumer Photo Collections using Markov Logic

Parag Singla
and
Henry Kautz
and
Jiebo Luo
and
Andrew Gallagher
### Abstract:

We identify the social relationships between individuals
in consumer photos. Consumer photos generally do not
contain a random gathering of strangers but rather groups
of friends and families. Detecting and identifying these relationships
are important steps towards understanding consumer
image collections. Similar to the approach that a human
might use, we use a rule-based system to quantify the
domain knowledge (e.g. children tend to be photographed
more often than adults; parents tend to appear with their
kids). The weight of each rule reflects its importance in the
overall prediction model. Learning and inference are based
on a sound mathematical formulation using the theory developed
in the area of statistical relational models. In particular,
we use the language called Markov Logic [14]. We
evaluate our model using cross validation on a set of about
4500 photos collected from 13 different users. Our experiments
show the potential of our approach by improving the
accuracy (as well as other statistical measures) over a set of
two different relationship prediction tasks when compared
with different baselines. We conclude with directions for
future work.
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