Abstract
The correntropy is originally proposed to measure the similarity between two random variables and developed as a novel metrics for feature matching. As a kernel method, the parameter of kernel function is very important for correntropy metrics. In this paper, we propose an adaptive parameter selection strategy for correntropy metrics and deduce a close-form solution based on the Maximum Correntropy Criterion (MCC). Moreover, considering the correlation of localized features, we modify the classic correntropy into a block-wise metrics. We verify the proposed metrics in face recognition applications taking Local Binary Pattern (LBP) features. Combined with the proposed adaptive parameter selection strategy, the modified block-wise correntropy metrics could result in much better performance in the experiments.
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Tan, Y., Fang, Y., Li, Y., Dai, W. (2013). Adaptive Kernel Size Selection for Correntropy Based Metric. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_5
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DOI: https://doi.org/10.1007/978-3-642-37410-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37409-8
Online ISBN: 978-3-642-37410-4
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