Embedded learning for computerized production of movie trailers
Movie trailers are usually extracted from the most exciting, interesting, or other noteworthy parts of the movies in order to attract the audience and persuade them to see the film. At present, hand crafted movie trailers currently occupy almost all the filming market, which is costly and time consuming. In this paper, we propose an embedded learning algorithm to generate movie trailers automatically without human interventions. Firstly, we use CNN to extract features of candidate frames from the film by a rank tracing technique. Secondly, SURF algorithm is utilized to match the frames of the movie with the corresponding trailer, thus the labeled and unlabeled dataset are prepared. Thirdly, the mutual information theory is introduced into the embedded machine learning to formulate a new embedded classification algorithm and hence characterize similar key elements of the trailers. Finally, semi supervised support vector machine is applied as the classifier to obtain the satisfactory key frames to produce the predicted trailers. By treating several famous movies and their manual handling trailers as the ground truth, series of experiments are carried out, which indicate that our method is feasible and competitive, providing a good potential for promoting the rapid development of the film industry in terms of publicity, as well as providing users with possible solutions for filtering large amounts of Internet videos.
Multimedia Tools and Applications