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We propose a new approach to perform approximate similarity search in metric spaces. The idea at the basis of this technique is that when two objects are very close one to each other they 'see' the world around them in the same way. Accordingly, we can use a measure of dissimilarity between the view of the world, from the perspective of the two objects, in place of the distance function of the underlying metric space. To exploit this idea we represent each object of a dataset by the ordering of a number of reference objects of the metric space according to their distance from the object itself. In order to compare two objects of the dataset we compare the two corresponding orderings of the reference objects. We show that efficient and effective approximate similarity searching can be obtained by using inverted files, relying on this idea. We show that the proposed approach performs better than other approaches in literature.

Approximate similarity search in metric spaces using inverted files