Retrieval and browsing of database images based on image emphasis and appeal
First Claim
13. A method automatically organizing and retrieving images from an image database comprising:
- (a) clustering images within said database into image groups;
(b) selectively clustering ones of said groups into subgroups to produce a hierarchical tree of said groups and subgroups; and
(c) ranking images within said groups and said subgroups based upon image emphasis or image appeal wherein said ranking includes;
(1) computing one or more quantities related to one or more features in each image, said features including a content of said images;
(2) processing said quantities with a reasoning algorithm that is trained based on opinions of one or more human observers; and
(3) selecting one image with a highest rank as an emphasis image or appeal image for said group or said subgroup.
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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 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.
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Citations
37 Claims
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13. A method automatically organizing and retrieving images from an image database comprising:
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(a) clustering images within said database into image groups;
(b) selectively clustering ones of said groups into subgroups to produce a hierarchical tree of said groups and subgroups; and
(c) ranking images within said groups and said subgroups based upon image emphasis or image appeal wherein said ranking includes;
(1) computing one or more quantities related to one or more features in each image, said features including a content of said images;
(2) processing said quantities with a reasoning algorithm that is trained based on opinions of one or more human observers; and
(3) selecting one image with a highest rank as an emphasis image or appeal image for said group or said subgroup. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A computer program product for automatically organizing and retrieving images from an image database, said program product comprising:
- a computer readable storage medium having a computer program stored thereon for performing a method of;
(a) clustering images within said database; and
(b) ranking images within said clusters based upon image emphasis or image appeal, wherein said ranking includes;
(1) computing one or more quantities related to one or more features in each image, said features including a content of said images;
(2) processing said quantities with a reasoning algorithm; and
(3) selecting one image with a highest rank as an emphasis image or appeal image. - View Dependent Claims (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
- a computer readable storage medium having a computer program stored thereon for performing a method of;
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30-1. The method as claimed in claim 27, wherein said objective features include a representative quality in terms of color content.
Specification