Automatic shot selection is an important aspect of movie summarization that is helpful both to producers and to audiences, e.g., for market promotion or browsing purposes. However, most of the related research has focused on shot selection based on low-level video content, which disregards semantic information, or on narrative properties extracted from text, which requires the movie script to be available. In this work, semantic shot selection based on the narrative prominence of movie characters in both the visual and the audio modalities is investigated, without the need for additional data such as a script. The output is a movie summary that only contains video frames from selected movie shots. Selection is controlled by a user-provided shot retention parameter, that removes key-frames/key-segments from the skim based on actor face appearances and speech instances. This novel process (Multimodal Shot Pruning, or MSP) is algebraically modelled as a multimodal matrix Column Subset Selection Problem, which is solved using an evolutionary computing approach.
['Ioannis Mademlis', 'Anastasios Tefas', 'Nikos Nikolaidis', 'Ioannis Pitas']
Movie shot selection preserving narrative properties