Story-oriented Image Selection and Placement
This addresses the need for efficient creation of multimodal content like news articles or social media posts, but it is incremental as it builds on existing methods for image-text integration.
The paper tackles the problem of automatically selecting relevant images from a collection and placing them within a story for multimodal narration, using an unsupervised combinatorial optimization approach that integrates object recognition, user tags, and commonsense knowledge.
Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts. Among the various kinds of media (images, videos, graphics, icons, audio) used in such multimodal stories, images are the most popular. The selection of images from a collection - either author's personal photo album, or web repositories - and their meticulous placement within a text, builds a succinct multimodal commentary for digital consumption. In this paper we present a system that automates the process of selecting relevant images for a story and placing them at contextual paragraphs within the story for a multimodal narration. We leverage automatic object recognition, user-provided tags, and commonsense knowledge, and use an unsupervised combinatorial optimization to solve the selection and placement problems seamlessly as a single unit.