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Rubik Gaussian-based patterns for dynamic texture classification
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DOI：10.1016/j.patrec.2020.04.007.png)
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摘要
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Illumination, noise, and changes of environments, scales negatively impact on encoding chaotic motions for dynamic texture (DT) representation. This paper proposes a new method to overcome those issues by addressing the following novel concepts. First, different Gaussian-based kernels are taken into account as an effective filtered pre-processing with low computational cost to point out robust and invariant features. Second, a discriminative operator, named Local Rubik-based Pattern (LRP), is introduced to adequately capture both shape and motion cues of DTs by proposing a new concept of complemented components together with an effective encoding method. In addition, it also addresses a novel thresholding to take into account rich spatio-temporal relationships extracted from a new model of neighborhood supporting region. Finally, an efficient framework for DT description is presented by exploiting operator LRP for encoding various instances of Gaussian-based volumes in order to form a robust descriptor against noise, changes of illumination, scale, and environment. Experiments for DT classification on benchmark datasets have authenticated the interest of our proposal. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Dynamic texture
Dynamic texture classification
Gaussian filtering
DoG
LBP
Video representation
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3.3
论文数:
8.0K
被引数:
1.6W
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