Abstract: Along with the convenience of Internet,there are also objectionable contents,e.g.horror and violent video which would seriously affects children’s physical and psychological health.So,an effective objectionable video recognition algorithm is necessary for web filtering.Recently,the multi-instance learning(MIL)has been introduced for horror and violent video recognition and achieved impressed results.However,many non-objectionable frames and noises in objectionable video scene is an intractable problem that inevitably degrades the recognition performance.In this paper,we propose a new method based on MIL via discriminative bag feature mapping.In the proposed method,an instance selection framework based on self-expressive dictionary learning is designed in which all instances are chosen as dictionary atoms and the representative instances can be selected by dictionary learning.Then bag level feature is constructed via the selected representative instances.Experiments on horror and violent video data sets show that our method is effective and efficient for objectionable scene recognition.