Semantic Indexing for Efficient Retrieval of Multimedia Data

Xiaoqi Cao, Matthias Klusch

In: Procedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR). International Workshop on Adaptive Multimedia Retrieval (AMR-12) 10th October 24 Copenhagen Denmark Lecture Notes in Computer Science (LNCS) Springer 2012.


We present a novel approach, called SemI, to semantic indexing of annotated multimedia objects for their efficient retrieval. The generation of multimedia indexes with SemI relies on the semantic annotation of these objects with references to concepts formally defined in standard OWL2 and semantic services described in OWL-S. For scoring the annotated multimedia data in these indexes an appropriate semantic similarity measure makes use of approximated logical concept abduction in order to alleviate strict logical false negatives. Efficient query answering over SemI indexes is performed with the use of Fagin's threshold algorithm. The results of our comparative experimental evaluation reveals that SemI-enabled multimedia retrieval can significantly outperform representative approaches of LSA- and RDF-based semantic retrieval in this domain in terms of precision at recall, averaged precision and discounted cumulative gain.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence