Effective Similarity Search in Multimedia Databases using Multiple Representations

Abstract

Similarity search in large multimedia databases is an important issue in nowadays multimedia environment. Multimedia objects such as music videos usually consist of multiple representations such as audio or video features. Since each representation may be of significantly different quality for a given multimedia object, similarity search methods could greatly benefit from taking these multiple representations into account. An intelligent similarity search technique should consider all available representations of the database objects and should automatically choose the best representations), i.e. those representations that model the object in the best possible way. In this paper, we propose a novel approach for similarity search in multimedia databases taking multiple representations of multimedia objects into account. In particular, we present weighting functions to rate the significance of a feature of each representation for a given database object. This allows weighting each representation during query processing. A broad experimental evaluation shows the suitability and the effectiveness of multi-represented similarity search in video databases.