@article{MAKHILLJEAS201813515681,
    title = {Pornographic Video Detection Scheme Using Multimodal Features},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {5},
    pages = {1174-1182},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.1174.1182},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.1174.1182},
    author = {Kwang Ho and},
    keywords = {Pornographic video detection,multimodal features,convolutional neural network,optical flow,spectrogram,support vector machine},
    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.}
    }