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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References
Rubner, Y., Puzicha, J., Tomasi, C., Buhmann, J.: Empirical evaluation of dissimilarity measures for color and texture. Computer Vision and Image Understanding 84, 25–43 (2001)
Smyth, P.: Clustering sequences with hidden markov models. In: Advances in Neural Information Processing Systems, NIPS (1997)
Slonim, N., Tishby, N.: Agglomerative information bottleneck. In: Advances in Neural Information Processing Systems, NIPS (1999)
Zuo, W., Zhang, D., Wang, K.: An assembled matrix distance metric for 2dpca-based image recognition. Pattern Recognition Letters 27, 210–216 (2006)
Liu, C.: The bayes decision rule induced similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1086–1090 (2007)
Zhu, C., Wen, F., Sun, J.: A rank-order distance based clustering algorithm for face tagging. In: Computer Vision and Pattern Recognition, CVPR (2011)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.J.: Distance metric learning with application to clustering with side-information. In: Advances in Neural Information Processing Systems, NIPS (2002)
Jain, P., Kulis, B., Grauman, K.: Fast image search for learned metrics. In: Computer Vision and Pattern Recognition, CVPR (2008)
Santamaria, I., Pokharel, P.P., Principe, J.C.: Generalized correlation function: definition, properties, and application to blind equalization. IEEE Transactions on Signal Processing 54, 2187–2197 (2006)
Liu, W., Pokharel, P.P., Principe, J.C.: Correntropy: A localized similarity measure. In: International Joint Conference on Neural Networks (2006)
Liu, W., Pokharel, P.P., Principe, J.C.: Correntropy: Properties and applications in non-gaussian signal processing. IEEE Transactions on Signal Processing 55, 5286–5298 (2007)
Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: International Conference on Computer Vision (2005)
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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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