  Repositories of images with semantic and emotion content descriptions are
valuable tools in many areas such as Affective Computing and Human-Computer
Interaction, but they are also important in the development of multimodal
searchable online databases. Ever growing number of image documents available
on the Internet continuously motivates research of better annotation models and
more efficient retrieval methods which use mash-up of available data on
semantics, scenes, objects, events, context and emotion. Formal knowledge
representation of such high-level semantics requires rich, explicit, human but
also machine-processable information. To achieve these goals we present an
online ontology-based image annotation tool WNtags and demonstrate its
usefulness in knowledge representation and image retrieval using the
International Affective Picture System database. The WNtags uses WordNet as
image tagging glossary but considers Suggested Upper Merged Ontology as the
preferred upper labeling formalism. The retrieval is performed using node
distance metrics to establish semantic relatedness between a query and the
collaboratively weighted tags describing high-level image semantics, after
which the result is ranked according to the derived importance. We also
elaborate plans to improve the WNtags to create a collaborative Web-based
multimedia repository for research in human emotion and attention.
