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Frequency-Based Deformable Models as a Distance Metric

This paper introduces a distance metric between two distributions that we call the Deformation Distance (DeD). The DeD is based on the “energy” that must be paid to deform one distribution into the other, presenting a perceptual similarity match better than other distribution distances. The DeD relies on the distribution’s frequencybased features. The frequency-based features are extracted by a physics-based deformable model that parameterizes the distribution. The DeD was evaluated on a variety of random as well as real distributions. Also, DeD was evaluated on image clustering and compared to other distances. The experimental results demonstrate the efficiency of the proposed distance metric.