Is similarity search useful for high dimensional spaces?

Abstract

In recent years, multimedia content-based retrieval has become an important research problem. In order to provide effective and also efficient access to relevant data stored in large (often distributed) digital repositories, advanced software tools are necessary. Content-based retrieval works on the idea of abstracting the contents of an object, for example color or shape in the case of images, by so-called features-features are typically points in a high-dimensional vector space. Instead of determining the similarity of two objects based on their raw data, only the much smaller feature representations are used to estimate the objects' similarity. Given a reference (query) object represented by its features, similarity predicates are defined to retrieve a specific number of best cases or all objects satisfying a (distance) constraint. In this respect, we can distinguish between similarity range and nearest neighbor (NN) queries.