基于高斯混合模型的分量对称正定矩阵模型

doi:10.16451/j.cnki.issn1003-6059.201811002

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Abstract The Gaussian mixture model(GMM) can use multiple Gaussian components to capture the variation information of image sets, and therefore it is a fine description method for image sets. Combining image set component symmetric positive definite descriptor]a component symmetric positive definite(SPD]model based on Gaussian mixture model(G-CSPD) is proposed. The image set is divided into sub-image sets with the same size, and the Gaussian mixture model of each sub-image set is calculated. A G-CSPD matrix in the form of the kernel matrix for all the sub-image sets is obtained, and the element in the matrix is used to denote the similarity between sub-image sets. The experimental results of 4 classification algorithms on 3 image sets show that G-CSPD is a more discriminative representation method for image sets.

Key words： Gaussian Mixture Model(GMM) Symmetric Positive Definite(SPD) Riemann Manifold Image Set Classification

Received: 27 July 2018

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61672265,61373055)

Corresponding Authors: WU Xiaojun]Ph.D.,professor.His research interests include artificial intelligence, pattern recognition and computer vision.

About author:: CHU Li, master student. Her research interests include Riemannian manifold and feature extraction.

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CHU Li,WU Xiaojun. Component Symmetric Positive Definite Descriptor Based on Gaussian Mixture Model[J]. , 2018, 31(11): 979-985.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201811002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I11/979

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