Design and performance evaluation of temporal motion and color energy features for objectionable video classification

With the increasing demand for access of mobile Internet via smartphones, the needs of adult images and videos filtering is escalating. In this paper, we propose a simple and fast objectionable video classification scheme using temporal motion and color energy features (TMCEF) and evaluate the performance on accuracy and processing time. The video segments extracted from a video clip have similar color and motion characteristics. System framework for TMCEF consists of key frame extraction, motion energy calculation, skin color energy calculation, and feature extraction based on statistical distribution metric using mean, standard deviation, and frequency analysis using discrete cosine transform (DCT). The motion energy is extracted based on foreground motion detection scheme and the color energy based on skin color detection method. In order to verify the performance of these video-based temporal features, support vector machine (SVM) classifier is used. In experiments, 64F-TMCEF (36) (the case of extracting 64 key frames and using 36 temporal motion and color energy features) yields the best performance on accuracy. However, due to excessive processing time of 64F-TMCEF, 36F-TMCEF (36) (the case of extracting 36 key frames and using 36 temporal motion and color energy features) is more practical. For smartphone applications, 16F-TCEF (18) (the case of extracting 16 key frames and using 18 temporal color energy features) is adequate enough to use.