Efficient similarity search using the Earth Mover's Distance for large multimedia databases

Authors:
Ira Assent;Marc Wichterich;Tobias Meisen;Thomas Seidl
Affiliations:
Data management and exploration group, RWTH Aachen University, Germany. assent@cs.rwth-aachen.de;Data management and exploration group, RWTH Aachen University, Germany. wichterich@cs.rwth-aachen.de;Data management and exploration group, RWTH Aachen University, Germany. meisen@cs.rwth-aachen.de;Data management and exploration group, RWTH Aachen University, Germany. seid1@cs.rwth-aachen.de
Venue:
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Year:
2008

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Abstract

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.