An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison

  • Authors:

  • Haibin Ling;Kazunori Okada

  • Affiliations:

  • IEEE;IEEE

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2007

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Abstract

We propose EMD-L_{1}: a fast and exact algorithm for computing the Earth Mover's Distance (EMD) between a pair of histograms. The efficiency of the new algorithm enables its application to problems that were previously prohibitive due to high time complexities. The proposed EMD-L_{1} significantly simplifies the original linear programming formulation of EMD. Exploiting the L_{1} metric structure, the number of unknown variables in EMD-L_{1} is reduced to O(N) from O(N^{2}) of the original EMD for a histogram with N bins. In addition, the number of constraints is reduced by half and the objective function of the linear program is simplified. Formally, without any approximation, we prove that the EMD-L_{1} formulation is equivalent to the original EMD with a L_{1} ground distance. To perform the EMD-L_{1} computation, we propose an efficient tree-based algorithm, Tree-EMD. Tree-EMD exploits the fact that a basic feasible solution of the simplex algorithm-based solver forms a spanning tree when we interpret EMD-L_{1} as a network flow optimization problem. We empirically show that this new algorithm has an average time complexity of O(N^{2}), which significantly improves the best reported supercubic complexity of the original EMD. The accuracy of the proposed methods is evaluated by experiments for two computation-intensive problems: shape recognition and interest point matching using multidimensional histogram-based local features. For shape recognition, EMD-L_{1} is applied to compare shape contexts on the widely tested MPEG7 shape data set, as well as an articulated shape data set. For interest point matching, SIFT, shape context and spin image are tested on both synthetic and real image pairs with large geometrical deformation, illumination change, and heavy intensity noise. The results demonstrate that our EMD-L_{1}-based solutions outperform previously reported state-of-the-art features and distance measures in solving the two tasks.