The nude image detection

Jiann Shu Lee; Pau-Choo Chung; Yun Ming Kuo; E. Liang Chen

The nude image detection

Jiann Shu Lee, Pau-Choo Chung, Yun Ming Kuo, E. Liang Chen

Institute of Computer and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a new nude image detection method. The foreground-background co-sampling mechanism is proposed to online sample the target image's chromatic distribution. The sampled data is then fed into a multiplayer perceptrons neural network to decide if the target image contains skin color object and which color category it belongs to. The texture feature, coarseness, is also utilized to acquire accurate skin segmentation. Several low-level whereas reliable geometrical constraints are further employed to inspect the skin regions. Finally, the mug shot exclusion procedure is applied to promote the system performance. The experimental results show our method is satisfactory for nude image detection.

Original language	English
Title of host publication	Sixth IASTED International Conference on Signal and Image Processing
Editors	M.H. Hamza
Pages	145-150
Number of pages	6
Publication status	Published - 2004 Dec 27
Event	Sixth IASTED International Conference on Signal and Image Processing - Honolulu, HI, United States Duration: 2004 Aug 23 → 2004 Aug 25

Publication series

Name	Sixth IASTED International Conference on Signal and Image Processing

Other

Other	Sixth IASTED International Conference on Signal and Image Processing
Country	United States
City	Honolulu, HI
Period	04-08-23 → 04-08-25

All Science Journal Classification (ASJC) codes

Engineering(all)

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Cite this

@inproceedings{3953172d931d4ec587f2e38602330ce7,

title = "The nude image detection",

abstract = "This paper presents a new nude image detection method. The foreground-background co-sampling mechanism is proposed to online sample the target image's chromatic distribution. The sampled data is then fed into a multiplayer perceptrons neural network to decide if the target image contains skin color object and which color category it belongs to. The texture feature, coarseness, is also utilized to acquire accurate skin segmentation. Several low-level whereas reliable geometrical constraints are further employed to inspect the skin regions. Finally, the mug shot exclusion procedure is applied to promote the system performance. The experimental results show our method is satisfactory for nude image detection.",

author = "Lee, {Jiann Shu} and Pau-Choo Chung and Kuo, {Yun Ming} and Chen, {E. Liang}",

year = "2004",

month = dec,

day = "27",

language = "English",

isbn = "088986442X",

series = "Sixth IASTED International Conference on Signal and Image Processing",

pages = "145--150",

editor = "M.H. Hamza",

booktitle = "Sixth IASTED International Conference on Signal and Image Processing",

note = "Sixth IASTED International Conference on Signal and Image Processing ; Conference date: 23-08-2004 Through 25-08-2004",

}

TY - GEN

T1 - The nude image detection

AU - Lee, Jiann Shu

AU - Chung, Pau-Choo

AU - Kuo, Yun Ming

AU - Chen, E. Liang

PY - 2004/12/27

Y1 - 2004/12/27

N2 - This paper presents a new nude image detection method. The foreground-background co-sampling mechanism is proposed to online sample the target image's chromatic distribution. The sampled data is then fed into a multiplayer perceptrons neural network to decide if the target image contains skin color object and which color category it belongs to. The texture feature, coarseness, is also utilized to acquire accurate skin segmentation. Several low-level whereas reliable geometrical constraints are further employed to inspect the skin regions. Finally, the mug shot exclusion procedure is applied to promote the system performance. The experimental results show our method is satisfactory for nude image detection.

AB - This paper presents a new nude image detection method. The foreground-background co-sampling mechanism is proposed to online sample the target image's chromatic distribution. The sampled data is then fed into a multiplayer perceptrons neural network to decide if the target image contains skin color object and which color category it belongs to. The texture feature, coarseness, is also utilized to acquire accurate skin segmentation. Several low-level whereas reliable geometrical constraints are further employed to inspect the skin regions. Finally, the mug shot exclusion procedure is applied to promote the system performance. The experimental results show our method is satisfactory for nude image detection.

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M3 - Conference contribution

AN - SCOPUS:10444270061

SN - 088986442X

T3 - Sixth IASTED International Conference on Signal and Image Processing

SP - 145

EP - 150

BT - Sixth IASTED International Conference on Signal and Image Processing

A2 - Hamza, M.H.

T2 - Sixth IASTED International Conference on Signal and Image Processing

Y2 - 23 August 2004 through 25 August 2004

ER -