Processing complex similarity queries with distance-based access methods

Efficient evaluation of similarity queries is one of the basic requirements for advanced multimedia applications. In this paper, we consider the relevant case where complex similarity queries are defined through a generic language L: and whose predicates refer to a single feature F. Contrary to the language level which deals only with similarity scores, the proposed evaluation process is based on distances between feature values-known spatial or metric indexes use distances to evaluate predicates. The proposed solution suggests that the index should process complex queries as a whole, thus evaluating multiple similarity predicates at a time. The flexibility of our approach is demonstrated by considering three different similarity languages, and showing how the M-tree access method has been extended to this purpose. Experimental results clearly show that performance of the extended M-tree is consistently better than that of state-of-the-art search algorithms.