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.

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