In this paper we describe the participation of TELECOM ParisTech in the Large Scale Visual Concept Detection and Annotation Task at the ImageClef 2009 challenge. This year, the focus was in the extension of (i) the amount of data available for training and testing, and (ii) the number of concepts to be annotated. We use Canonical Correlation Analysis in order to infer a latent space where text and visual description are highly correlated. Starting from a visual description of a test image, we first map it into the latent space, then we predict the underlying text features (and also annotations) which best fit the visual ones in the latent space. Our method is very fast while achieving good results.