  In this paper, we address a problem of managing tagged images with hybrid
summarization. We formulate this problem as finding a few image exemplars to
represent the image set semantically and visually, and solve it in a hybrid way
by exploiting both visual and textual information associated with images. We
propose a novel approach, called homogeneous and heterogeneous message
propagation ($\text{H}^\text{2}\text{MP}$). Similar to the affinity propagation
(AP) approach, $\text{H}^\text{2}\text{MP}$ reduce the conventional
\emph{vector} message propagation to \emph{scalar} message propagation to make
the algorithm more efficient. Beyond AP that can only handle homogeneous data,
$\text{H}^\text{2}\text{MP}$ generalizes it to exploit extra heterogeneous
relations and the generalization is non-trivial as the reduction to scalar
messages from vector messages is more challenging. The main advantages of our
approach lie in 1) that $\text{H}^\text{2}\text{MP}$ exploits visual similarity
and in addition the useful information from the associated tags, including the
associations relation between images and tags and the relations within tags,
and 2) that the summary is both visually and semantically satisfactory. In
addition, our approach can also present a textual summary to a tagged image
collection, which can be used to automatically generate a textual description.
The experimental results demonstrate the effectiveness and efficiency of the
roposed approach.
