Simultaneous image tagging and geo-location prediction within hypergraph ranking framework

The development of social media has led to a burst of interest in image-related metadata information, such as tags and geo-tags. Tags are semantic keywords that are assigned to an image. Image tagging enables the users of social media sharing platforms to annotate images, facilitating image search and content description. Despite the volume of related research, issues such as accuracy or efficiency still remain open problems. Here, a novel method for simultaneous image tagging and geo-location prediction is proposed that is based on hypergraph learning. The method is further improved by enforcing group sparsity constraints. It fully exploits various types of information, such as social, image-related metadata, or similarities based on visual attributes. Experiments on a dataset crawled from Flickr demonstrate F₁ at 10 top ranked tags equal to 0.558 for image tagging and cumulative geotagging prediction rate at 3 top ranks equal to 83%.