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A symmetric KL divergence based spatiogram similarity measure

Spatiogram is a generalization of histogram to capture higher-order spatial moments information. To apply spatiogram to object tracking, suitable similarity measure is critical. Although there is a series of work on introducing improved distance measure over the original method, their performance in object tracking is very limited due to insufficient discriminative power. In this paper, we present a symmetric KL divergence based spatiogram similarity measure and show both theoretically and experimentally that, the proposed measure gives superior discriminative power than existing methods, and achieved promising performance in tracking object from single or sequence of images.