Classification between natural and graphics images based on generalized Gaussian distributions

被引：6

作者：

Morinaga, Atsushi ^{[1
]}

Hara, Kenji ^{[1
]}

Inoue, Kohei ^{[1
]}

Urahama, Kiichi ^{[1
]}

机构：

[1] Kyushu Univ, Dept Commun Design Sci, Minami Ku, 4-9-1 Shiobaru, Fukuoka, Fukuoka 8158540, Japan

来源：

INFORMATION PROCESSING LETTERS | 2018年 / 138卷

关键词：

Design of algorithms; Natural image; CG image; Generalized Gaussian distribution; Maximum likelihood estimation; DENSITY;

D O I：

10.1016/j.ipl.2018.05.010

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a novel method for classifying photographic and computer-generated images based on generalized Gaussian distribution (GGD) modeling of subband coefficients. The estimated shape and standard deviation parameters of GGD within each resolution level, the ratio of the estimated shape parameters between different resolution levels, and the ratio of the estimated standard deviation parameters between different resolution levels are used as features for the classification. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：31 / 34

页数：4

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