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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Obscenity detection using haar-like features and Gentle Adaboost classifier.

Rashed Mustafa1, Yang Min2, Dingju Zhu3

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China ; University of Chinese Academy of Sciences, Beijing 100049, China ; Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh.

Thescientificworldjournal
|July 9, 2014
PubMed
Summary

This study introduces a novel method for detecting nipples in images to identify pornography, improving accuracy and efficiency. The research utilizes Gentle Adaboost (GAB) haar-cascade classifiers for robust detection of erotogenic human body parts.

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Area of Science:

  • Computer Vision
  • Image Analysis
  • Machine Learning

Background:

  • Traditional methods for detecting obscene content often rely on large skin exposure, leading to false positives and missed detections of partially exposed erotogenic areas.
  • Accurate identification of pornographic material requires focusing on specific erotogenic body parts, such as nipples, rather than general skin exposure.

Purpose of the Study:

  • To develop and evaluate a novel method for detecting nipples within images to more accurately identify pornographic content.
  • To enhance the robustness and efficiency of pornographic image detection systems by focusing on erotogenic human body parts.

Main Methods:

  • Implementation of a novel nipple detection method using Gentle Adaboost (GAB) haar-cascade classifiers and haar-like features.
  • Integration of a skin filter as a preprocessing step to improve system robustness.
  • Comparison of haar-cascade and train-cascade classifiers for detection accuracy and processing time.

Main Results:

  • The haar-cascade classifier achieved a high detection rate of 0.9875 but had a longer detection time (0.162 seconds).
  • The train-cascade classifier offered a suitable detection time (0.127 seconds) with a detection rate of 0.8429.
  • The system demonstrated improved robustness through the use of a skin filter prior to nipple detection.

Conclusions:

  • The proposed nipple detection method offers a more precise approach to identifying pornographic content compared to methods based solely on skin exposure.
  • Both haar-cascade and train-cascade classifiers show promise, with haar-cascade excelling in accuracy and train-cascade in speed, offering options for different application needs.
  • Further development in this area can lead to more effective and efficient automated content moderation systems.