Abstract: In a method for varying one or more print attributes of a print made from a digital image, a print attribute value is computed for the digital image based on a determination of the degree of importance, interest or attractiveness of the image and the resulting print attribute value is used to control a print attribute of the print made from the image. The print attribute value is derived from an emphasis or appeal value, wherein an appeal value is an assessment of each image taken by itself and the emphasis value is an assessment of each image in relation to other images in a group. In a typical embodiment, the print attribute is either the number of prints made of the image, the size of a print made from the image, or the magnification factor used for the image.

Abstract: A method for varying the image processing path for a digital image involves the steps of (a) computing an image processing attribute value for the digital image based on a determination of the degree of importance, interest or attractiveness of the image; and (b) using the image processing attribute value to control the image processing path for the image. In one embodiment, the image processing attribute value is based on an appeal value determined from the degree of importance, interest or attractiveness that is intrinsic to the image. In another embodiment, wherein the image is one of a group of digital images, the image processing attribute value is based on an emphasis value determined from the degree of importance, interest or attractiveness of the image relative to other images in the group of images.

Abstract: An image is automatically assessed with respect to certain features, wherein the assessment is a determination of the degree of importance, interest or attractiveness of the image. First, a digital image is obtained corresponding to the image. Then one or more quantities are computed that are related to one or more features in the digital image, including one or more features pertaining to the content of the digital image. The quantities are processed with a reasoning algorithm that is trained on the opinions of one or more human observers, and an output is obtained from the reasoning algorithm that assesses the image. More specifically, the reasoning algorithm is a Bayesian network that provides a score which, when done for a group of images, selects one image as the emphasis image. The features pertaining to the content of the digital image include people-related features and/or subject-related features.

Abstract: A method, image recognition system, computer program, etc., for detecting sky regions in an image comprise classifying potential sky pixels in the image by color, extracting connected components of the potential sky pixels, eliminating ones of the connected components that have a texture above a predetermined texture threshold, computing desaturation gradients of the connected components, and comparing the desaturation gradients of the connected components with a predetermined desaturation gradient for sky to identify true sky regions in the image.

Abstract: A method for detecting a main subject in an image, the method comprises: receiving a digital image; extracting regions of arbitrary shape and size defined by actual objects from the digital image; grouping the regions into larger segments corresponding to physically coherent objects; extracting for each of the regions at least one structural saliency feature and at least one semantic saliency feature; and integrating saliency features using a probabilistic reasoning engine into an estimate of a belief that each region is the main subject.