  Creating a cohesive, high-quality, relevant, media story is a challenge that
news media editors face on a daily basis. This challenge is aggravated by the
flood of highly relevant information that is constantly pouring onto the
newsroom. To assist news media editors in this daunting task, this paper
proposes a framework to organize news content into cohesive, high-quality,
relevant visual storylines. First, we formalize, in a nonsubjective manner, the
concept of visual story transition. Leveraging it, we propose four graph-based
methods of storyline creation, aiming for global story cohesiveness. These were
created and implemented to take full advantage of existing graph algorithms,
ensuring their correctness and good computational performance. They leverage a
strong ensemble-based estimator which was trained to predict story transition
quality based on both the semantic and visual features present in the pair of
images under scrutiny. A user study covered a total of 28 curated stories about
sports and cultural events. Experiments showed that (i) visual transitions in
storylines can be learned with a quality above 90%, and (ii) the proposed graph
methods can produce cohesive storylines with quality in the range of 88% to
96%.
