A computational approach to the art of visual storytelling

For millennia, humanity as been using images to tell stories. In modern society, these visual narratives take the center stage in many different contexts, from illustrated children’s books to news media and comic books. They leverage the power of compounding various images in sequence to present compelling and informative narratives, in an immediate and impactful manner. In order to create them, many criteria are taken into account, from the quality of the individual images to how they synergize with one another. With the rise of the Internet, visual content with which to create these visual storylines is now in abundance. In areas such as news media, where visual storylines are regularly used to depict news stories, this has both advantages and disadvantages. Although content might be available online to create a visual storyline, filtering the massive amounts of existing images for high quality, relevant ones is a hard and time consuming task. Furthermore, combining these images into visually and semantically cohesive narratives is a highly skillful process and one that takes time. As a first step to help solve this problem, this thesis brings state of the art computational methodologies to the age old tradition of creating visual storylines. Leveraging these methodologies, we define a three part architecture to help with the creation of visual storylines in the context of news media, using social media content. To ensure the quality of the storylines from a human perception point of view, we deploy methods for filtering and raking images according to news quality standards, we resort to multimedia retrieval techniques to find relevant content and we propose a machine learning based approach to organize visual content into cohesive and appealing visual narratives