Adult Image Detection Using Bayesian Decision Rule Weighted by SVM Probability

Choi, Byeong-Cheol; Chung, Byungho; Ryou, Jae-Cheol

doi:10.1109/iccit.2009.43

Cited by 16 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approach based on textual cues has been utilized [2], it searched keywords near the web images to find pornographic contents. 15 Recent developments in computer vision have motivated many image contentbased methods for pornographic content detection (could be roughly divided into rule-based methods [3], image retrieval-based methods [4] [5] and learning-based methods [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]). These methods are based on the rich visual information extracted from the original images (e.g.…”

Section: Introductionmentioning

confidence: 99%

Pornographic image detection utilizing deep convolutional neural networks

Nian

Wang

et al. 2016

Neurocomputing

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Pornographic image detection utilizing deep convolutional neural networks

Nian

Wang

et al. 2016

Neurocomputing

View full text Add to dashboard Cite

“…15,28,75,91,98,101,118,136,143 The features extracted from the skin map include average skin probability of the whole image, height and width of the largest region of skin, average skin probability inside the largest skin region, number of skin regions in the image, distance from the centroid of the largest skin region to the center of the image, edge of connected components of skin, percentage of pixels detected as skin, and number of connected components of skin. The extracted skin regions and feature vectors are useful for image classi¯cation and will help the generic algorithm to decide.…”

Section: Features Extraction Processmentioning

confidence: 98%

“…Technology companies devoted to image recognition acknowledge that the technology for nudity detection is still crude, yielding inaccurate results at the cost of computing power. 9,10,28,50,136 One desired property of such a system is the possibility of dynamically changing the content-type that is¯ltered. Di®erent clients may require di®erent strict content¯lters (e.g.…”

Section: Content-based Techniquementioning

confidence: 99%

See 1 more Smart Citation

An Automated Anti-Pornography System Using a Skin Detector Based on Artificial Intelligence: A Review

Zaidan

Karim

Ahmad

et al. 2013

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

Unprecedented advances in Internet technologies with multimedia capabilities have enabled pornography and adult content to be widely and freely distributed as easy as a click of a mouse through various means such as YouTube, Facebook, and Tags.Protecting children from unnecessary exposure to adult content has, therefore, become a serious problem in the real world. In particular, the considerable perversion in pornography and the exposure of children and the society to such perversions leads to moral decay.Constructing an appropriate¯lter for pornographic images is a major concern in modern society;however, this area poses challenges.This study aims to shed light on a content-based technique that employs an anti-pornography machine and to encourage researchers to study this adult image¯ltering technique. In this study, we discuss models of skin detection and their advantages and disadvantages in real life. We also elaborate on the pornographic image classi¯er using a feature extraction process and its classi¯cation process, along with the possible di±culties it may present. This study also analyzes anti-pornography techniques based on skin detection and discusses their strengths and weaknesses.

show abstract

“…In case of the positive answer, skin segmentation is applied to determine the exact boundaries of the detected skin regions. Applications of skin detection and segmentation are of a wide range and significance, and they include gesture recognition for human-computer interaction [3], objectionable content filtering [4], content-based image retrieval [5], medical imaging [6,7], and image coding [8].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive algorithm for segmenting skin regions

Kawulok

Nalepa

et al. 2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

show abstract

Adult Image Detection Using Bayesian Decision Rule Weighted by SVM Probability

Cited by 16 publications

References 4 publications

Pornographic image detection utilizing deep convolutional neural networks

Pornographic image detection utilizing deep convolutional neural networks

An Automated Anti-Pornography System Using a Skin Detector Based on Artificial Intelligence: A Review

Self-adaptive algorithm for segmenting skin regions

Contact Info

Product

Resources

About