Classification between natural and graphics images based on generalized Gaussian distributions

Atsushi Morinaga; Kenji Hara; Kohei Inoue; Kiichi Urahama

doi:10.1016/j.ipl.2018.05.010

Classification between natural and graphics images based on generalized Gaussian distributions

Atsushi Morinaga, Kenji Hara, Kohei Inoue, Kiichi Urahama

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	31-34
Number of pages	4
Journal	Information Processing Letters
Volume	138
DOIs	https://doi.org/10.1016/j.ipl.2018.05.010
Publication status	Published - Oct 2018

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Signal Processing
Information Systems
Computer Science Applications

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10.1016/j.ipl.2018.05.010

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title = "Classification between natural and graphics images based on generalized Gaussian distributions",

abstract = "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.",

author = "Atsushi Morinaga and Kenji Hara and Kohei Inoue and Kiichi Urahama",

year = "2018",

month = oct,

doi = "10.1016/j.ipl.2018.05.010",

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AU - Morinaga, Atsushi

AU - Hara, Kenji

AU - Inoue, Kohei

AU - Urahama, Kiichi

PY - 2018/10

Y1 - 2018/10

N2 - 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.

AB - 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.

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DO - 10.1016/j.ipl.2018.05.010

M3 - Article

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VL - 138

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JO - Information Processing Letters

JF - Information Processing Letters

ER -

Classification between natural and graphics images based on generalized Gaussian distributions

Abstract

All Science Journal Classification (ASJC) codes

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