Don't mention the shoe! A learning to rank approach to content selection for image description generation

We tackle the sub-task of content selection as part of the broader challenge of automatically generating image descriptions. More specifically, we explore how decisions can be made to select what object instances should be mentioned in an image description, given an image and labelled bounding boxes. We propose casting the content selection problem as a learning to rank problem, where object instances that are most likely to be mentioned by humans when describing an image are ranked higher than those that are less likely to be mentioned. Several features are explored: those derived from bounding box localisations, from concept labels, and from image regions. Object instances are then selected based on the ranked list, where we investigate several methods for choosing a stopping criterion as the ‘cut-off’ point for objects in the ranked list. Our best-performing method achieves state-of-the-art performance on the ImageCLEF2015 sentence generation challenge.