An image analyzing and expression adding apparatus for presenting an operator with image impressions displayed in terms of sensitive language and for allowing the operator to use stored design know-how in adding expressions to the image on display. An image segmenting unit segments an input image into a plurality of areas. An image feature storing unit stores the physical feature quantities of each of the segmented areas. The physical feature quantities are processed by an image analyzing unit preparing visual feature quantities about the entire image. A sensitive influence quantity computing unit receives the visual feature quantities thus prepared and, based on information from a design know-how storing unit, computes factors of the terms representing sensitivity. The operator is presented with the factors of the sensitivity-expressing terms. In response, the operator instructs desired expressions using the sensitivity-expressing terms through an expression instructing unit. An image expression adding unit receives the instructions about the expressions and modifies the image by referring to the information from the design know-how storing unit. The physical feature quantities of the segmented areas are also modified immediately, so that the impressions of the modified image are analyzed and displayed.
A document data processor system includes means for evaluating a user's impression of a layout of a document, and means for storing the evaluation information. The system also includes means for storing document design knowledge regarding the layout of multiple standard documents. The system allows a user to create a new document based on the stored document design knowledge and the stored evaluation information so that new documents are created in accordance with user's desires, as reflected by previous document evaluations. In addition, a draft document that is being created may be re-formatted based on a user's evaluation of the draft.
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.
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 or the appeal image. The features pertaining to the content of the digital image include people-related features and/or subject-related features. Moreover, additional quantities may be computed that relate to objective measures of the digital image, such as colorfulness and/or sharpness.
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. Moreover, additional quantities may be computed that relate to objective measures of the digital image, such as colorfulness and/or sharpness.
A method to automatically vary the compression of images by ranking images within clusters based upon image emphasis. The ranking process computes one or more quantities related to one or more features in each image. The features can include the content of images. The invention processes the quantities with a reasoning algorithm that is trained based on opinions of one or more human observers. The invention applies the quantities to the images to produce the ranking and variably compresses the images depending upon the ranking. The images having a low ranking and are compressed more than images that have a high ranking.
