基于独立子空间分析的不良视频检测方法

Objectionable Video Detection Method Based on Independent Subspace Analysis

为了检测网络中含有不良内容的视频,提出一种基于非监督学习特征的不良视频检测方法。该方法使用独立子空间分析网络对未标定视频进行训练,学习视频中的运动模式,使用训练好的网络对待测视频提取运动特征。该特征结合词袋模型,通过支持向量机的分类实现不良视频的检测。相比传统的光流、运动直方图等人工设计的特征,该特征计算效率高,且检测效果对视频质量不敏感。在视频库上进行实验后,发现该方法对不良视频的检测准确率相较于对比算法提高约10%。

In order to detect objectionable videos on the Internet,a detection method for objectionable video based on unsupervised learning features is proposed.The Independent Subspace Analysis（ISA）network is trained upon a set of unlabeled videos to learn motion patterns.The well trained network can be employed to extract motion features in the videos to be detected.Combined with bag of words,the motion features help classify objectionable videos from normal ones by Support Vector Machine.Compared with traditional hand-designed features such as optical flow and motion histogram,the proposed features have high computing efficiency,besides they are not sensitive to video qualities.After the experiment on the video library,it is found that the objectionable videos detection accuracy rate of the proposed method is promoted by about 10% than that of the approach in comparison.