Automatic Nipple Detection Using Cascaded AdaBoost Classifier

Many non-pornographic images containing large exposure of skin area or approximate skin-color area are prone to be detected as the pornographic images. This paper proposes a novel method based on AdaBoost algorithm for nipple detection of pornographic images. The AdaBoost algorithm has excellent per...

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Bibliographic Details
Main Authors: Xin Kejun, Wu Jian, Ni Pengyu, Huang Jie
Format: Conference Proceeding
Language:English
Subjects:
AdaBoost learning algorithm
Haar-like feature
color features
texture features
shape features
cascaded AdaBoost classifier
Classification algorithms
Feature extraction
Gray-scale
Image color analysis
Shape
Skin
Training
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Summary:Many non-pornographic images containing large exposure of skin area or approximate skin-color area are prone to be detected as the pornographic images. This paper proposes a novel method based on AdaBoost algorithm for nipple detection of pornographic images. The AdaBoost algorithm has excellent performance in both detection accuracy and detection speed. The method extracts extended Haar-like features, color features, texture features and shape features to train and obtain a cascaded AdaBoost classifier by using AdaBoost algorithm. And it is validated for locating nipple existence in pornographic images. The experimental results show that this method performs well for nipple detection in pornographic images, and can reduce effectively the false positive rate against the non-pornographic images.
DOI:10.1109/ISCID.2012.262