A chi-squared-transformed subspace of LBP histogram for visual recognition

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Jianfeng RenJunsong Yuan

Abstract

Local binary pattern (LBP) and its variants have been widely used in many recognition tasks. Subspace approaches are often applied to the LBP feature in order to remove unreliable dimensions, or to derive a compact feature representation. It is well-known that subspace approaches utilizing up to the second-order statistics are optimal only when the underlying distribution is Gaussian. However, due to its nonnegative and simplex constraints, the LBP feature deviates significantly from Gaussian distribution. To alleviate this problem, we propose a chi-squared transformation (CST) to transfer the LBP feature to a feature that fits better to Gaussian distribution. The proposed CST leads to the formulation of a two-class classification problem. Due to its asymmetric nature, we apply asymmetric principal component analysis (APCA) to better remove the unreliable dimensions in the CST feature space. The proposed CST-APCA is evaluated extensively on spatial LBP for face recognition, protein cellular classification, and spatial-temporal LBP for dynamic texture recognition. All experiments show that the proposed feature transformation significantly enhances the recognition accuracy.

References

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Jun 26, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jianfeng RenJunsong Yuan
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Feb 1, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Yang Xiao Junsong Yuan

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Citations

Feb 18, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Tuan D Pham
Jan 27, 2021·Multimedia Tools and Applications·Manish KumarAman Singh
Oct 2, 2021·Scientific Reports·Yan HuJiang Liu

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