US6847733B2
Expired / Lapsed
US
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Retrieval and browsing of database images based on image emphasis and appeal
Assignee: Monument Peak Ventures LLC
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Filing: 2001-05-23
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Granted: 2005-01-25
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Expires: 2021-05-23
Abstract
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.
Inventors
- 👤 Andreas E. Savakis
- 👤 Rajiv Mehrotra
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 10:05
CPC Classifications
G; G06; G06F; G06F16/00; G06F16/50; G06F16/58; G06F16/583; G06V; G06V20/00; G06V20/10; G06V40/00; G06V40/10; G06V40/16; G06V40/161; G06V40/162
Claims Analysis
Total Claims: 41
Claim 1
Independent Claim
85 words
A method for automatically organizing and retrieving images from an image database comprising: (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 an appeal image.
Claim 2
Independent Claim
85 words
A method for automatically organizing and retrieving images from an image database comprising: (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 an appeal image.
Claim 3
Dependent Claim
21 words
The method as claimed in claim 1 , wherein said features include at least one of people-related features and subject-related features.
Claim 4
Dependent Claim
33 words
The method as claimed in claim 1 , wherein said computing further includes computing one or more quantities related to one or more objective features pertaining to objective measures of the digital image.
Claim 5
Dependent Claim
20 words
The method as claimed in claim 3 , wherein said objective features include at least one of colorfulness and sharpness.
Claim 6
Dependent Claim
21 words
The method as claimed in claim 3 , wherein said objective features include a representative quality in terms of color content.
Claim 7
Dependent Claim
19 words
The method as claimed in claim 3 , wherein said objective features include a uniqueness of picture aspect format.
Claim 8
Dependent Claim
21 words
The method as claimed in claim 1 , wherein said reasoning algorithm is trained from ground truth studies of candidate images.
Claim 9
Dependent Claim
16 words
The method as claimed in claim 1 , wherein the reasoning algorithm comprises a Bayesian network.
Claim 10
Dependent Claim
21 words
The method as claimed in claim 1 , wherein said clustering is based upon one or more of color and texture.
Claim 11
Dependent Claim
39 words
The method as claimed in claim 1 , further comprising, before said clustering, performing an initial image emphasis process comprising said steps (1)-(2), wherein said clustering is based at least partially upon results of said initial image emphasis process.
Claim 12
Dependent Claim
51 words
The method as claimed in claim 1 , further comprising: (c) selecting a highest ranking emphasis image or appeal image group based on said ranking; (d) repeating said (a) clustering and said (b) ranking of images within said database using said highest ranking emphasis or appeal image group as a query.
Claim 13
Dependent Claim
29 words
The method as claimed in claim 1 , wherein said clustering produces multiple clusters of said images and said selecting produces a representative image for each of said clusters.
Claim 14
Independent Claim
126 words
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.
Claim 15
Independent Claim
126 words
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.
Claim 16
Dependent Claim
26 words
The method as claimed in claim 13 , wherein said process of selectively clustering said groups into subgroups is based upon a size of said groups.
Claim 17
Dependent Claim
24 words
The method as claimed in claim 13 , further comprising selectively clustering said subgroups into additional subgroups based upon a size of said subgroups.
Claim 18
Dependent Claim
21 words
The method as claimed in claim 13 , wherein said features include at least one of people-related features and subject-related features.
Claim 19
Dependent Claim
33 words
The method as claimed in claim 13 , wherein said computing further includes computing one or more quantities related to one or more objective features pertaining to objective measures of the digital image.
Claim 20
Dependent Claim
20 words
The method as claimed in claim 17 , wherein said objective features include at least one of colorfulness and sharpness.
Claim 21
Dependent Claim
21 words
The method as claimed in claim 17 , wherein said objective features include a representative quality in terms of color content.
Claim 22
Dependent Claim
19 words
The method as claimed in claim 17 , wherein said objective features include a uniqueness of picture aspect format.
Claim 23
Dependent Claim
21 words
The method as claimed in claim 13 , wherein said reasoning algorithm is trained from ground truth studies of candidate images.
Claim 24
Dependent Claim
16 words
The method as claimed in claim 13 , wherein the reasoning algorithm comprises a Bayesian network.
Claim 25
Dependent Claim
21 words
The method as claimed in claim 13 , wherein said clustering is based upon one or more of color and texture.
Claim 26
Dependent Claim
42 words
The method as claimed in claim 13 , further comprising, before said clustering of said images, performing an initial image emphasis process comprising said steps (1)-(2), wherein said clustering is based at least partially upon results of said initial image emphasis process.
Claim 27
Dependent Claim
54 words
The method as claimed in claim 13 , further comprising: (d) selecting a highest ranking emphasis or appeal image group based on said ranking; (e) performing said (a) clustering, said (b) selectively clustering, and said (b) ranking of images within said database using said highest ranking emphasis or appeal image group as a query.
Claim 28
Dependent Claim
29 words
The method as claimed in claim 13 , wherein said clustering produces multiple clusters of said images and said selecting produces a representative image for each of said clusters.
Claim 29
Dependent Claim
105 words
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.
Claim 30
Dependent Claim
105 words
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.
Claim 31
Dependent Claim
23 words
The computer program product as claimed in claim 27 , wherein said features include at least one of people-related features and subject-related features.
Claim 32
Dependent Claim
35 words
The computer program product as claimed in claim 27 , wherein said computing further includes computing one or more quantities related to one or more objective features pertaining to objective measures of the digital image.
Claim 33
Dependent Claim
21 words
The computer program product as claimed in claim 27 , wherein said objective features include a uniqueness of picture aspect format.
Claim 34
Dependent Claim
23 words
The computer program product as claimed in claim 27 , wherein said reasoning algorithm is trained from ground truth studies of candidate images.
Claim 35
Dependent Claim
18 words
The computer program product as claimed in claim 27 , wherein the reasoning algorithm comprises a Bayesian network.
Claim 36
Dependent Claim
23 words
The computer program product as claimed in claim 27 , wherein said clustering is based upon one or more of color and texture.
Claim 37
Dependent Claim
41 words
The computer program product as claimed in claim 27 , further comprising, before said clustering, performing an initial image emphasis process comprising said steps (1)-(2), wherein said clustering is based at least partially upon results of said initial image emphasis process.
Claim 38
Dependent Claim
52 words
The computer program product as claimed in claim 27 , further comprising: (c) selecting a highest ranking emphasis or appeal image group based on said ranking; (d) repeating said (a) clustering and said (b) ranking of images within said database using said highest ranking emphasis or appeal image group as a query.
Claim 39
Dependent Claim
31 words
The computer program product as claimed in claim 27 , wherein said clustering produces multiple clusters of said images and said selecting produces a representative image for each of said clusters.
Claim 40
Dependent Claim
22 words
The computer program product as claimed in claim 27 , wherein said objective features include at least one of colorfulness and sharpness.
Claim 41
Dependent Claim
23 words
The computer program product as claimed in claim 27 , wherein said objective features include a representative quality in terms of color content.
References Cited (Backward Citations)
US5418895A
Cited
US5424945A
Cited
US5875265A
Cited
US5953451A
Cited
US6285995B1
Cited
US6345274B1
Cited
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| 3.5 Years (Year 4) | 2008-01-25 | 2008-07-26 | 2009-01-25 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2012-01-25 | 2012-07-26 | 2013-01-25 | $3760.00 | Window Closed / Lapsed |
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