TY  - JOUR
T1  - Pornographic Video Detection Scheme Using Multimodal Features
AU - Song, Kwang Ho AU - Kim, Yoo-Sung 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 5
SP  - 1174
EP  - 1182
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.1174.1182
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.1174.1182
KW  - Pornographic video detection
KW  -multimodal features
KW  -convolutional neural network
KW  -optical flow
KW  -spectrogram
KW  -support vector machine
AB  - In this study, we propose the new pornographic video detection scheme using multimodal feature
such as image features of each frame using deep learning architecture, image descriptor features of the frame
sequence, motion features using optical flow and audio features extracted from video. By using these various
features at once we can detect almost all pornographic events without being confused by a specific element
of input video. And as the performance evaluation results we can obtain 100% true positive rate and 67.6%
overall accuracy. Although, the overall accuracy is little bit low due to high false positive rate we could
successfully detect the pornographic videos which are difficult to detect by using only single modal features.
ER  -