Understanding the Message of Images with Knowledge Base Traversals.

The message of news articles is often supported by the pointed use of iconic images. These images together with their captions encourage emotional involvement of the reader. Current algorithms for understanding the semantics of news articles focus on its text, often ignoring the image. On the other side, works that target the semantics of images, mostly focus on recognizing and enumerating the objects that appear in the image. In this work, we explore the problem from another perspective: Can we devise algorithms to understand the message encoded by images and their captions? To answer this question, we study how well algorithms can describe an image-caption pair in terms of Wikipedia entities, thereby casting the problem as an entity-ranking task with an image-caption pair as query. Our proposed algorithm brings together aspects of entity linking, subgraph selection, entity clustering, relatedness measures, and learning-to-rank. In our experiments, we focus on media-iconic image-caption pairs which often reflect complex subjects such as sustainable energy and endangered species. Our test collection includes a gold standard of over 300 image-caption pairs about topics at different levels of abstraction. We show that with a MAP of 0.69, the best results are obtained when aggregating content-based and graph-based features in a Wikipedia-derived knowledge base.