MUSE: Multi-Represented Similarity Estimation

In modern multimedia databases, objects can be specified by a large variety of feature representations. In this paper, we present a novel technique for multi-represented similarity estimation. We transform the distance between two objects in each representation into so-called similarity and dissimilarity estimates which are used to derive a meaningful similarity score. To determine the parameters for our new similarity measure, we present methods with and without user feedback.

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Hans-Peter Kriegel, Peter Kunath, Alexey Pryakhin, Matthias Schubert
• Keywords: Database, ICDE 2008, Meaningful Similarity Score, Multi-represented Similarity Estimation, So-called Similarity