  Nowadays, where multimedia data is continuously generated, stored, and
distributed, multimedia indexing, with its purpose of group- ing similar data,
becomes more important than ever. Understanding the gist (=message) of
multimedia instances is framed in related work as a ranking of concepts from a
knowledge base, i.e., Wikipedia. We cast the task of multimedia indexing as a
gist understanding problem. Our pipeline benefits from external knowledge and
two subsequent learning- to-rank (l2r) settings. The first l2r produces a
ranking of concepts rep- resenting the respective multimedia instance. The
second l2r produces a mapping between the concept representation of an instance
and the targeted class topic(s) for the multimedia indexing task. The
evaluation on an established big size corpus (MIRFlickr25k, with 25,000
images), shows that multimedia indexing benefits from understanding the gist.
Finally, with a MAP of 61.42, it can be shown that the multimedia in- dexing
task benefits from understanding the gist. Thus, the presented end-to-end
setting outperforms DBM and competes with Hashing-based methods.
