Automatic Nipple Detection Using Shape and Statistical Skin Color Information

Wang, Yue; Li, Jun; Wang, HeeLin; Hou, Zujun

doi:10.1007/978-3-642-11301-7_63

Cited by 17 publications

(16 citation statements)

References 9 publications

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“…This study used reduction of false skin segmentation based on the GLCM texture features homogeneity and correlation, followed by linear SVM classification. This was not done in related studies, such as by Wang, et al [2], Karavarsamis, et al [10], Basilio, et al [6]. Santos, et al used GLCM features based on LDA classification [4].…”

Section: Discussionmentioning

confidence: 99%

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Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine

Nugroho

Rahadian

Adji

et al. 2016

J. Eng. Technol. Sci.

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Abstract. Nowadays, the Internet is one of the most important things in a human's life. The unlimited access to information has the potential for people to gather any data related to their needs. However, this sophisticated technology also bears a bad side, for instance negative content information. Negative content can come in the form of images that contain pornography. This paper presents the development of a skin classification scheme as part of a negative content filtering system. The data are trained by grey-level co-occurrence matrices (GLCM) texture features and then used to classify skin color by support vector machine (SVM). The tests on skin classification in the skin and non-skin categories achieved an accuracy of 100% and 97.03%, respectively. These results indicate that the proposed scheme has potential to be implemented as part of a negative content filtering system.

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Section: Discussionmentioning

confidence: 99%

“…Wang, et al [2] applied Viola Jones classification to detect nipple areas. Unfortunately, a number of false positives were detected in the eyes and navel areas.…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine

Nugroho

Rahadian

Adji

et al. 2016

J. Eng. Technol. Sci.

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“…So far this paper was the only paper on nipple detection. In Wang et al (2010) proposed another robust method entitled "Automatic Nipple Detection Using Shape and Statistical Skin Color Information". In this study a new approach on nipple detection for adult content recognition presented and it combines the advantage of…”

Section: Erotogenic Body Parts Detectionmentioning

confidence: 99%

Objectionable Image Detection in Cloud Computing Paradigm-a Review

Mustafa¹

2013

Journal of Computer Science

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Obscenity detection from images and videos are now crucial due to social and ethical reasons. It has been two decades the research on this field started. Most of the works are based on skin color detection, which are not suitable for finding obscenity. The reason for this is that, there are many skins like objects such as beach photos, human skin like animal's fur, skin colored painting that enables false positive and negative rate. In addition all works performed well on some particular set of images or video data. In this research some aspects of obscenity detection is described delineating strength, weakness and possible extensions of prior works. Introducing some new features and incorporation of multiple classifiers and transfer learning will lead the work more robust. Moreover, traditional multimedia cloud computing has been investigated in this study and proposed some new research ideas.

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“…The most typical means of identifying pornography are strongly based in skin detection [21,6,22]. In the case of videos, the most common approach is basically an extension of the image detection techniques [7,8,9] applied for a set of selected frames representative of the clip known as key frames.…”

Section: Literature Reviewmentioning

confidence: 99%

Adult Video Content Detection Using Machine Learning Techniques

Ochoa

Yayilgan

Cheikh

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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Automatic adult video detection is a problem of interest for many organizations around the globe aiming to restrict the availability of potentially harmful material for young audiences. Being most of the existing techniques a mere extension of the image categorization problem. In the present work we employ video genre classification techniques applied specifically for adult content detection by considering cinematic principles. Shot structure and camera motion in the temporal domain are used as the main features while skin detection and color histograms representation in the spatial domain are utilized as complementary features. Using a data set of more than 7 hours of video, our experiments comparing two different SVM algorithms achieve a high accuracy of 94.44%.

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Automatic Nipple Detection Using Shape and Statistical Skin Color Information

Cited by 17 publications

References 9 publications

Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine

Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine

Objectionable Image Detection in Cloud Computing Paradigm-a Review

Adult Video Content Detection Using Machine Learning Techniques

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