Selecting vantage objects for similarity indexing

Indexing has become a key element in the pipeline of a multimedia retrieval system, due to continuous increases in database size, data complexity, and complexity of similarity measures. The primary goal of any indexing algorithm is to overcome high computational costs involved with comparing the query to every object in the database. This is achieved by efficient pruning in order to select only a small set of candidate matches. Vantage indexing is an indexing technique that belongs to the category of embedding or mapping approaches, because it maps a dissimilarity space onto a vector space such that traditional access methods can be used for querying. Each object is represented by a vector of dissimilarities to a small set of m reference objects, called vantage objects. Querying takes place within this vector space. 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 that addresses the retrieval performance directly, and present extensive experimental results based on three data sets of different size and modality, including a comparison with other selection strategies. The results clearly demonstrate both the efficacy and scalability of the proposed approach.