Improving performance of X-rated video classification with the optimized repeated curve-like spectrum feature and the skip-and-analysis processing

Abstract

This paper addresses the automatic classification of X-rated videos by analyzing its obscene sounds. In this paper, we propose the optimized repeated curve-like spectrum feature for classifying obscene sounds and the skip-and-analysis processing for classifying videos. The optimized repeated curve-like spectrum feature uses the longer frame size for stationary frequency region based on the fact that most of obscene sounds, such as sexual moans and screams, consist of mostly vowels and the variation of syllables occurs slowly compared to general speech. It also uses the customized mel-scaled bandpass filter for the valid frequency regions of obscene sounds with the frequency contents mainly under 5 kHz. The skip-and-analysis processing is based on the video playback characteristics that a harmful or normal scene continues to be played at least for certain duration of time during a playback. When the skip-and-analysis processing is applied, clips to be analyzed are selected by skip interval values and only these selected clips are used to classify videos. The processing performances of the optimized repeated curve-like spectrum feature have improvements from 21 % to 25.6 % compared to the repeated curve-like spectrum feature without degradation of classification performance in clip-level classification. Furthermore, when the skip-and-analysis processing is applied, the processing performance of classifying is improved significantly by from 82.59 % to 95.03 % maintaining the classification performance of more than 90 % at F1-score.