1. A method for extracting an object of interest out of each image in a group of digital images that contain the object of interest, comprising: (a) providing a group of digital images, each containing the object of interest with a background; (b) selecting a seed image from the group of digital images, and displaying the seed image to a user; (c) the user providing at least one marking for the seed image, which corresponds to a subset of pixels in the seed image that indicates whether the set of pixels belongs to a part of object of interest or a part of the background; (d) producing from the seed image and the marking a statistical model that can be used for separating the object of interest from the background in the group of images, wherein the statistical model includes: (i) learned likelihood for a feature vector belonging to foreground and background; and (ii) learned distances or affinities between features such that these distances reflect the input received via the user marking; and (e) applying the statistical model to each image in the group of digital images to produce a cutout of the object of interest from each digital image.