The research on video has been developing along
with the research of the digital image processing and
technological advances. The development of the internet has led
to the increased production of negative images and video content.
There are many challenges faced in creating filtering systems for
negative content, especially on video. Most researches on the
negative content filtering have been based on the skin
segmentation of an image.
In this research, the method of negative content detection on
video (porn video) based on the skin segmentation in the video
composer frame was developed. By combining the two color
spaces namely RGB and YCbCr color space as the skin detection
algorithms to improve the accuracy of the class determination on
the video. The sampling method used in this research was the 8
bytes keyframe extraction or about 256 frames with a certain
distance based on the total video frame. Based on the number of
frames extracted, the porn percentage value was calculated. The
data were 102 videos with duration ranging between 2-15
minutes each. The datasets were divided into 60 data, namely 30
porn and 30 nonporn video. The other 42 data were used for
accuracy testing. The determination of classes was limited by a
porn percentage threshold value (pornographic %). Based on the
research, from the result yielded, the video threshold was
classified into porn class when the value of porn percentage
threshold ≥ 70, and nonporn class when the value of porn
percentage threshold < 70. The result of 42 video tests showed
the accuracy of about 90.5 %.
