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An Improved Audio Based Key Frame Detection Model for Movie Summarization

In the recent scenario, the amount of multimedia content mainly videos is increasing at a high rate. This makes the browsing, retrieval and delivery of videos a very slow and difficult task. Video summarization helps in faster browsing and content indexing of large videos by producing an abstract overview of the whole video. Movie summarization condenses a full length movie into a summary which includes the relevant contents in the movie. The proposed framework implements a movie summarization method based on the audio and video features of a movie. The method comprises of three phases. In the pre-processing phase, audio and frames of the video are extracted and stored. Next in the audio feature analysis phase, audio data having high frequency value are extracted. Three techniques are proposed for audio frame analyses which are high frequency method (HFM), Low frequency method (LFM) and threshold frequency method (TFM). In summary generation phase, key frames are extracted which is further refined in the post processing phase. The experimental results indicate that in majority of the cases HFM achieves better performance than LFM and TFM in both qualitative and quantitative analysis.