14 May 2015 Mutual information for enhanced feature selection in visual tracking
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In this paper we investigate the problem of fusing a set of features for a discriminative visual tracking algorithm, where good features are those that best discriminate an object from the local background. Using a principled Mutual Information approach, we introduce a novel online feature selection algorithm that preserves discriminative features while reducing redundant information. Applying this algorithm to a discriminative visual tracking system, we experimentally demonstrate improved tracking performance on standard data sets.
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Victor Stamatescu, Sebastien Wong, David Kearney, Ivan Lee, Anthony Milton, "Mutual information for enhanced feature selection in visual tracking", Proc. SPIE 9476, Automatic Target Recognition XXV, 947603 (14 May 2015); doi: 10.1117/12.2176556; https://doi.org/10.1117/12.2176556

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