Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
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.
How to cite this article:
Kwang Ho Song and Yoo-Sung Kim. Pornographic Video Detection Scheme Using Multimodal Features.
DOI: https://doi.org/10.36478/jeasci.2018.1174.1182
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2018.1174.1182