This paper presents a novel approach for temporal and semantic segmentation of edited videos into meaningful segments, from the point of view of the storytelling structure. The objective is to decompose a long video into more manageable sequences, which can in turn be used to retrieve the most significant parts of it given a textual query and to provide an effective summarization. Previous video decomposition methods mainly employed perceptual cues, tackling the problem either as a story change detection, or as a similarity grouping task, and the lack of semantics limited their ability to identify story boundaries. Our proposal connects together perceptual, audio and semantic cues in a specialized deep network architecture designed with a combination of CNNs which generate an appropriate embedding, and clusters shots into connected sequences of semantic scenes, i.e. stories. A retrieval presentation strategy is also proposed, by selecting the semantically and aesthetically "most valuable" thumbnails to present, considering the query in order to improve the storytelling presentation. Finally, the subjective nature of the task is considered, by conducting experiments with different annotators and by proposing an algorithm to maximize the agreement between automatic results and human annotators.
We present a newly developed strategy for automatically segmenting movies into logical story units. A logical story unit can be understood as an approximation of a movie episode, which is a high-level temporal movie segment, characterized either by a single event (dialog, action scene, etc.) or by several events taking place in parallel. Since we consider a whole event and not a single shot to be the most natural retrieval unit for the movie category of video programs, the proposed segmentation is the crucial first step toward a concise and comprehensive content-based movie representation for browsing and retrieval purposes. The automation aspect is becoming increasingly important with the rising amount of information to be processed in video archives of the future. The segmentation process is designed to work on MPEG-DC sequences, where we have taken into account that at least a partial decoding is required for performing content-based operations on MPEG compressed video streams. The proposed technique allows for carrying out the segmentation procedure in a single pass through a video sequence.