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To make similarity searching in multimedia databases practical, indexing has become a necessity. Vantage indexing is an indexing technique which maps a dissimilarity space onto a vector space such that each object is represented by a vector of dissimilarities to a small set of m reference objects, the vantage objects. Querying takes place within this vector space, reducing the number of distance calculations to m. The retrieval performance of a system based on this technique can be improved significantly through a proper choice of vantage objects. We propose a new technique for selecting vantage objects and present experimental results based on data sets of different modality.

To make similarity searching in multimedia databases practical, indexing has become a necessity. Vantage indexing is an indexing technique which maps a dissimilarity space onto a vector space such that each object is represented by a vector of dissimilarities to a small set of m reference objects, the vantage objects. Querying takes place within this vector space, reducing the number of distance calculations to m. The retrieval performance of a system based on this technique can be improved significantly through a proper choice of vantage objects. We propose a new technique for selecting vantage objects and present experimental results based on data sets of different modality.

Abstract