An apparatus for semi-automatically segmenting an image object from the remainder of the image, comprising: (a) a processor configured for image processing of captured color digital images and receiving a user selection input as a user selected location within said color image for identifying an object to which segmentation is to be applied; and (b) a memory storing instructions; (c) said instructions when executed by the processor performing segmenting of the object as a foreground from its background in said color image, comprising: (i) downscaling (16, 20) said color image to form a downscaled color image; (ii) iteratively (94, 110, 114) performing a segmentation of the object in the downscaled color image for different sizes of an initial mask around the user selected location until the segmentation is successful or a maximum number of iterations has been reached, the segmentation of the object in the downscaled color image for an initial mask comprising performing an expectation-maximization, EM, classification using the initial mask with a joint Bayes classifier for classifying nearby pixels in the downscaled color image as foreground or background, and simultaneously estimating class parameters and an object mask for the object in response to utilizing pixel color values of each pixel, wherein the segmentation of the object in the downscaled color image for an initial mask is successful if the EM classification converges and fails if at least one stopping criteria is met; (iii) outputting (34, 106) an object mask for the successfully segmented object; (iv) upscaling the object mask and selecting a finer resolution of the color image corresponding to a resolution of the upscaled object mask; and (v) further segmenting the object using the upscaled object mask and the selected finer resolution color image. (a) a processor configured for image processing of captured color digital images and receiving a user selection input as a user selected location within said color image for identifying an object to which segmentation is to be applied; and (b) a memory storing instructions; (c) said instructions when executed by the processor performing segmenting of the object as a foreground from its background in said color image, comprising: (i) downscaling (16, 20) said color image to form a downscaled color image; (ii) iteratively (94, 110, 114) performing a segmentation of the object in the downscaled color image for different sizes of an initial mask around the user selected location until the segmentation is successful or a maximum number of iterations has been reached, the segmentation of the object in the downscaled color image for an initial mask comprising performing an expectation-maximization, EM, classification using the initial mask with a joint Bayes classifier for classifying nearby pixels in the downscaled color image as foreground or background, and simultaneously estimating class parameters and an object mask for the object in response to utilizing pixel color values of each pixel, wherein the segmentation of the object in the downscaled color image for an initial mask is successful if the EM classification converges and fails if at least one stopping criteria is met; (iii) outputting (34, 106) an object mask for the successfully segmented object; (iv) upscaling the object mask and selecting a finer resolution of the color image corresponding to a resolution of the upscaled object mask; and (v) further segmenting the object using the upscaled object mask and the selected finer resolution color image. (i) downscaling (16, 20) said color image to form a downscaled color image; (ii) iteratively (94, 110, 114) performing a segmentation of the object in the downscaled color image for different sizes of an initial mask around the user selected location until the segmentation is successful or a maximum number of iterations has been reached, the segmentation of the object in the downscaled color image for an initial mask comprising performing an expectation-maximization, EM, classification using the initial mask with a joint Bayes classifier for classifying nearby pixels in the downscaled color image as foreground or background, and simultaneously estimating class parameters and an object mask for the object in response to utilizing pixel color values of each pixel, wherein the segmentation of the object in the downscaled color image for an initial mask is successful if the EM classification converges and fails if at least one stopping criteria is met; (iii) outputting (34, 106) an object mask for the successfully segmented object; (iv) upscaling the object mask and selecting a finer resolution of the color image corresponding to a resolution of the upscaled object mask; and (v) further segmenting the object using the upscaled object mask and the selected finer resolution color image.