Image annotation and two paths to text illustration
Abstract
This paper describes our participation to the ImageCLEF 2016 scalable concept image annotation main task and Text Illustration teaser. Regarding image annotation, we focused on better localizing the detected features. For this, we identified the saliency of the image to collect a list of potential interesting places into the image. We also added a specific human attribute detector that boosted the results of the best performing team in 2015. For the text illustration, we proposed two complementary approaches. The first one relies on semantic signatures that give a textual description of an image. This description is further matched to the textual query. The second approach learns a common latent space, in which visual and textual features are directly comparable. We propose a robust description, as well as the use of an auxiliary dataset to improve retrieval. While the first approach only uses external data, the second one was mainly learned from the provided training dataset.