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Discriminative local binary pattern

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Abstract

Local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective features with a low computational cost. There, however, are some limitations mainly regarding sensitivity to noise and loss of image contrast information. In this paper, we propose a novel LBP-based feature extraction method to remedy those drawbacks without degrading the simplicity of the original LBP formulation. LBP is built upon encoding local pixel intensities into binary patterns which can be regarded as separating them into two modes (clusters). We introduce Fisher discriminant criterion to optimize the LBP coding for exploiting binary patterns more stably and discriminatively with robustness to noise. Besides, image contrast information is incorporated in a unified way by leveraging the discriminant score as a weight on the binary pattern; therefore, the prominent patterns, such as around edges, are emphasized. The proposed method is applicable to extract not only image features but also motion features by both efficiently decomposing a XYT volume patch into 2-D patches and employing the effective thresholding strategy based on the volume patch. In the experiments on various visual recognition tasks, the proposed method exhibits superior performance compared to the ordinary LBP and the other methods.

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Notes

  1. 58 patterns for \(N=8\) consist of 1 flat pattern for zero 0/1 transition, 56 moderate patterns for less than or equal to twice transitions and 1 messy pattern for greater than twice transitions. In \(N=9\), we consider 1 flat and 1 messy patterns no matter what the center pixel is, and \(112=56\times 2\) moderate patterns according to the center pixel state.

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Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, 1-1-1 Umezono, Tsukuba, Japan

    Takumi Kobayashi

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  1. Takumi Kobayashi
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Correspondence to Takumi Kobayashi.

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Kobayashi, T. Discriminative local binary pattern. Machine Vision and Applications 27, 1175–1186 (2016). https://doi.org/10.1007/s00138-016-0780-8

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