title: On Markov Earth Mover's Distance.
abstract: In statistics, pattern recognition and signal processing, it is of utmost importance to have an effective and efficient distance to measure the similarity between two distributions and sequences. In statistics this is referred to asgoodness-of-fit problem. Two leading goodness of fit methods are chi-square and Kolmogorov-Smirnov distances. The strictly localized nature of these two measures hinders their practical utilities in patterns and signals where the sample size is usually small. In view of this problem Rubner and colleagues developed the earth mover's distance (EMD) to allow for cross-bin moves in evaluating the distance between two patterns, which find a broad spectrum of applications. EMD-L1 was later proposed to reduce the time complexity of EMD from super-cubic by one order of magnitude by exploiting the special L1 metric. EMD-hat was developed to turn the global EMD to a localized one by discarding long-distance earth movements. In this work, we introduce a Markov EMD (MEMD) by treating the source and destination nodes absolutely symmetrically. In MEMD, like hat-EMD, the earth is only moved locally as dictated by the degree d of neighborhood system. Nodes that cannot be matched locally is handled by dummy source and destination nodes. By use of this localized network structure, a greedy algorithm that is linear to the degree d and number of nodes is then developed to evaluate the MEMD. Empirical studies on the use of MEMD on deterministic and statistical synthetic sequences and SIFT-based image retrieval suggested encouraging performances.
authors.affiliation: Department of Computer Science, City College of New York, New York, New York 10031, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Wei
keywords: Goodness-of-fit; content-based image retrieval; earth mover’s distance; greedy algorithm; linear programming; pattern matching