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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Ortho-diffusion decompositions of graph-based representation of images
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Ortho-diffusion decompositions of graph-based representation of images

机译:基于图的图像表示的正扩散分解

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摘要

This paper proposes a new feature representation methodology for graph-based data. Initially, random walks on matrices of pairwise data similarities are considered. A diffusion process is embedded into orthonormal decompositions of such matrices at various scales while enabling a data reduction mechanism as well. At each scale, the QR orthonormal decomposition algorithm, alternating with diffusions and data reduction stages is applied recursively on the given graph-based data representations. The proposed methodology is used in extracting complex feature representations from images, which are then used for image matching and in face recognition. In the face recognition application, both global appearance models and semantic representations of biometric features are considered. Both the correlation and the covariance of images of human faces are considered for the training stage when using global appearance models. The proposed data representation is shown to be robust in face recognition applications, when face images are represented in low resolution and when they are corrupted by noise. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文提出了一种新的基于图形的数据特征表示方法。最初,考虑在成对数据相似性矩阵上的随机游动。扩散过程被嵌入各种尺度的此类矩阵的正交分解中,同时还启用了数据缩减机制。在每个尺度上,将QR正交分解算法(与扩散和数据缩减阶段交替使用)递归应用于给定的基于图的数据表示形式。所提出的方法用于从图像中提取复杂的特征表示,然后将其用于图像匹配和面部识别。在人脸识别应用程序中,要同时考虑全局外观模型和生物特征的语义表示。使用全局外观模型时,在训练阶段要考虑人脸图像的相关性和协方差。当面部图像以低分辨率表示并且当它们被噪声破坏时,所提出的数据表示在面部识别应用中被证明是鲁棒的。 (C)2015 Elsevier Ltd.保留所有权利。

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