Multimedia similarity search in large databases requires efficient query processing. The Earth Mover's Distance, introduced in computer vision, is successfully used as a similarity model in a number of small-scale applications. Its computational complexity hindered its adoption in large multimedia databases.

We enable directly indexing the Earth Mover's Distance in structures such as the R-tree and the VA-file by providing the accurate 'MinDist' function to any bounding rectangle in the index. We exploit the computational structure of the new MinDist to derive a new lower bound for the EMD MinDist which is assembled from quantized partial solutions yielding very fast query processing times. We prove completeness of our approach in a multistep scheme. Extensive experiments on real world data demonstrate the high efficiency.