Distribution-Based Similarity for Multi-Represented Multimedia Object
In modern multimedia databases, objects can be represented by a large variety of feature representations. In order to employ all available information in a best possible way, a joint statement about object similarity must be derived. In this paper, the authors present a novel technique for multi-represented similarity estimation which is based on probability distributions modeling the connection between the distance value and object similarity. To tune these distribution functions to model the similarity in each representation, they propose a bootstrapping approach maximizing the agreement between the distributions.