Digital imaging has vastly increased users' ability to amass very large numbers of still images, video image sequences, and multimedia records combining one or more images and other content. (Still images, video sequences, and multimedia records are referred to collectively herein with the term “image records”.) With very large numbers of image records, organization becomes difficult.
Efforts have been made to aid users in organizing and utilizing image records by assigning metadata to individual image records that indicates a metric of expected value to the user. For example, the V-550 digital camera, marketed by Eastman Kodak Company of Rochester, N.Y., includes a user control labeled “Share”, which can be actuated by the user to designate a respective image for preferential printing and e-mailing. This approach is useful, but is limited by the metric being binary.
U.S. Patent Publication No. 2003/0128389 A1, filed by Matraszek et al., discloses another measure of image record importance, “affective information”, which can take the form of a multi-valued metadata tag. The affective information can be a manual entry or can automatically detect user reactions, including user initiated utilization of a particular image, such as how many times an image was printed or sent to others via e-mail. In either case, affective information is identified with a particular user. This approach is useful, but complex if user reactions are automatically detected. There is also the risk of user reactions being ambiguous.
U.S. Pat. No. 6,671,405 to Savakis et al, discloses another approach, which computes a metric of “emphasis and appeal” of an image, without user intervention. A first metric is based upon a number of factors, which can include: image semantic content (e.g. people, faces); objective features, such as colorfulness and sharpness; and main subject features, such as size of the main subject. A second metric compares the factors relative to other images in a collection. The factors are integrated using a trained reasoning engine. U.S. Patent Publication No. 2004/0075743 is somewhat similar and discloses image sorting of images based upon user-selected parameters of semantic content or objective features in the images. These approaches have the advantage of working from the images themselves and the shortcoming of being computationally intensive.
It would thus be desirable to provide a user value metric that has a low risk of ambiguity and that is not computationally intensive.