Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration’s content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights on movies. We demonstrate this model on two very popular movies.
Jung @cite_10 proposed a narrative structure graph to summarize a movie. The graph is composed of scene nodes which are narrative elements with character interactions, and connections between scenes decided by editorial relations. Using only scenes to construct a narrative structure graph for movie summarization is not sufficient. Indeed, story elements such as major characters and their interactions cannot be retrieved from this graph.
@cite_10
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A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding. In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs. The storyline graphs can be an effective summary that visualizes various branching narrative structure of events or activities recurring across the input photo sets of a topic class. In order to explore further the usefulness of the storyline graphs, we leverage them to perform the image sequential prediction tasks, from which photo recommendation applications can benefit. We formulate the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develop an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization. With experiments on more than 3.3 millions of images of 24 classes and user studies via Amazon Mechanical Turk, we show that the proposed algorithm improves other candidate methods for both storyline reconstruction and image prediction tasks. In this paper, we address the problem of jointly summarizing large sets of Flickr images and YouTube videos. Starting from the intuition that the characteristics of the two media types are different yet complementary, we develop a fast and easily-parallelizable approach for creating not only high-quality video summaries but also novel structural summaries of online images as storyline graphs. The storyline graphs can illustrate various events or activities associated with the topic in a form of a branching network. The video summarization is achieved by diversity ranking on the similarity graphs between images and video frames. The reconstruction of storyline graphs is formulated as the inference of sparse time-varying directed graphs from a set of photo streams with assistance of videos. For evaluation, we collect the datasets of 20 outdoor activities, consisting of 2.7M Flickr images and 16K YouTube videos. Due to the large-scale nature of our problem, we evaluate our algorithm via crowdsourcing using Amazon Mechanical Turk. In our experiments, we demonstrate that the proposed joint summarization approach outperforms other baselines and our own methods using videos or images only. Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. Our key insight is that the graph generation problem can be formulated as message passing between the primal node graph and its dual edge graph. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods on the Visual Genome dataset as well as support relation inference in NYU Depth V2 dataset.