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Hierarchical Gaussian Descriptors with Application to Person Re-Identification

Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id). Although a covariance descriptor has been successfully applied to person re-id, it loses the local structure of a region and mean information of pixel features, both of which tend to be the major discriminative information for person re-id. In this paper, we present novel meta-descriptors based on a hierarchical Gaussian distribution of pixel features, in which both mean and covariance information are included in patch and region level descriptions. More specifically, the region is modeled as a set of multiple Gaussian distributions, each of which represents the appearance of a local patch. The characteristics of the set of Gaussian distributions are again described by another Gaussian distribution. Because the space of Gaussian distribution is not a linear space, we embed the parameters of the distribution into a point of Symmetric Positive Definite (SPD) matrix manifold in both steps. We show, for the first time, that normalizing the scale of the SPD matrix enhances the hierarchical feature representation on this manifold. Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the SPD matrix descriptors. The experimental results conducted on five public datasets indicate the effectiveness of the proposed descriptors and the two types of normalizations.

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Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.42 No.9 September 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 2179 - 2194

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

GAUSSIAN DISTRIBUTION
PERSON RE-IDENTIFICATION
SYMMETRIC POSITIVE DEFINITE MATRICES
RIEMANNIAN GEOMETRY
IMAGE FEATURE DESCRIPTOR
LOG-EUCLIDEAN RIEMANNIAN METRIC

Info Detail Spesifik

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Pernyataan Tanggungjawab

Tetsu Matsukawa, Takahiro Okabe, Einoshin Suzuki, Yoichi Sato

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