Film segmentation and indexing using autoassociative neural networks

In this paper, Autoassociative Neural Network (AANN) models are explored for segmentation and indexing the films (movies) using audio features. A two-stage method is proposed for segmenting the film into sequence of scenes, and then indexing them appropriately. In the first stage, music and speech plus music segments of the film are separated, and music segments are labelled as title and fighting scenes based on their position. At the second stage, speech plus music segments are classified into normal, emotional, comedy and song scenes. In this work, Mel frequency cepstral coefficients (MFCCs), zero crossing rate and intensity are used as audio features for segmentation and indexing the films. The proposed segmentation and indexing method is evaluated on manual segmented Hindi films. From the evaluation results, it is observed that title, fighting and song scenes are segmented and indexed without any errors, and most of the errors are observed in discriminating the comedy and normal scenes. Performance of the proposed AANN models used for segmentation and indexing of the films, is also compared with hidden Markov models, Gaussian mixture models and support vector machines.