A method for constructing an image model (M1; M) from at least one image data input (IV1; IV1-IVn), comprises the steps of, in an iterative way, determining at least one state (PS1; PS1-PSn) of said at least one image data input (IV1; IV1-IVn), and a state (PSMF) of an intermediate learning model (MF; MIF) determining a target state (TSP) from said at least one state (PS1; PS1-PSn) of said at least one image data input, and from the state (PSMF) of said intermediate learning model (MF; MIF), performing at least one transformation in accordance with the determined target state (TSP) on said at least one image data input (IV1; IV1-IVn), thereby generating at least one transformed image (IV1T; IV1T-IVnT), aggregating said at least one transformed image (IV1T; IV1T-IVnt) with intermediate learning model (MF; MIF; MIT; MFT) information, thereby generating an updated estimate of said image model (M1; M), providing said updated estimate of said image model (M1; M) as said image model (M1; M) while also providing said updated estimate of said image model (M1; M) in a feedback loop to a model object learning module (500) for deriving an update of said intermediate learning model (MF, MIF).