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US6512850B2
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Method of and apparatus for identifying subsets of interrelated image objects from a set of image objects
Assignee: International Business Machines Corp
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Filing: 1998-12-09
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Granted: 2003-01-28
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Expires: 2018-12-09
Abstract
Abstract In one example given, a query for identifying interrelationships between image objects of a set of image objects is received from an input device. Each of a plurality of similarity values between all image objects of the set is compared with threshold criteria from the query. Clusters of image object identifiers are generated based on comparing and are visually displayed on a visual output device.
Inventors
- 👤 Alan Tsu-I Yaung
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 10:53
CPC Classifications
G; G06; G06F; G06F16/00; G06F16/50; G06F16/58; G06F16/583; G06F16/5838; G06V; G06V30/00; G06V30/10; G06V30/24; G06V30/242
Claims Analysis
Total Claims: 16
Claim 1
Independent Claim
185 words
A method of clustering image object identifiers, comprising: (a) comparing a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of a set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; (b) in response to determining that a similarity value between the first image object and a second image object meets the threshold criteria from comparing in (a), comparing the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set; and (c) in response to determining that a similarity value between the second image object and a third image object meets the threshold criteria from comparing in (b), logically grouping image object identifiers associated with the first, second, and third image objects into a cluster.
Claim 2
Independent Claim
185 words
A method of clustering image object identifiers, comprising: (a) comparing a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of a set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; (b) in response to determining that a similarity value between the first image object and a second image object meets the threshold criteria from comparing in (a), comparing the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set; and (c) in response to determining that a similarity value between the second image object and a third image object meets the threshold criteria from comparing in (b), logically grouping image object identifiers associated with the first, second, and third image objects into a cluster.
Claim 3
Dependent Claim
40 words
A method according to claim 1 , further comprising: (d) receiving a query from an input device, the query associated with the threshold criteria; and (e) visually displaying the logical grouping of image object identifiers at a visual output device.
Claim 4
Dependent Claim
19 words
A method according to claim 2 , wherein comparing in (a) is responsive to receiving the query in (d).
Claim 5
Dependent Claim
26 words
A method according to claim 2 , further comprising: generating the first and the second pluralities of similarity values responsive to receiving the query in (d).
Claim 6
Dependent Claim
164 words
Software for generating clusters of image object identifiers, comprising: primary compare software, said primary compare software executable to compare a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of the set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; and secondary compare software, said secondary compare software executable in response to determining from said primary compare software that a similarity value between the first image object and a second image object meets the threshold criteria, said secondary compare software executable to compare the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set.
Claim 7
Dependent Claim
164 words
Software for generating clusters of image object identifiers, comprising: primary compare software, said primary compare software executable to compare a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of the set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; and secondary compare software, said secondary compare software executable in response to determining from said primary compare software that a similarity value between the first image object and a second image object meets the threshold criteria, said secondary compare software executable to compare the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set.
Claim 8
Independent Claim
172 words
A method of clustering image object identifiers associated with a set of M image objects, comprising: for each image object J, where J=1 to M: assigning an image object identifier associated with an image object J to a cluster J; for each image object J, where J=1 to M: for each similarity rank between the image object J and an image object I, where I=J+1 and object image I is associated with an image object identifier assigned to a cluster I: comparing the similarity rank between the image objects I and J with a threshold criteria designated by a user in a query, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; if the similarity rank between the image objects I and J meets the threshold criteria: reassigning the image object identifier assigned to cluster I to cluster J; and after reassigning, setting the cluster I to an empty set.
Claim 9
Independent Claim
172 words
A method of clustering image object identifiers associated with a set of M image objects, comprising: for each image object J, where J=1 to M: assigning an image object identifier associated with an image object J to a cluster J; for each image object J, where J=1 to M: for each similarity rank between the image object J and an image object I, where I=J+1 and object image I is associated with an image object identifier assigned to a cluster I: comparing the similarity rank between the image objects I and J with a threshold criteria designated by a user in a query, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; if the similarity rank between the image objects I and J meets the threshold criteria: reassigning the image object identifier assigned to cluster I to cluster J; and after reassigning, setting the cluster I to an empty set.
Claim 10
Dependent Claim
122 words
A method according to claim 6 , wherein after the step of setting the cluster I to an empty set, the method further comprising: for each similarity rank between the image object I and an image object K, where K=1 to M except where K=J or K=I, and object image K is associated with at least one image object identifier assigned to a cluster K; comparing the similarity rank between the image objects I and K with the threshold criteria; if the similarity rank between the image objects I and K meets the threshold criteria: reassigning the at least one image object identifier assigned to cluster K to the cluster J; and after reassigning, setting the cluster K to an empty set.
Claim 11
Independent Claim
229 words
An apparatus, comprising: a similarity value generator, said similarity value generator operative to generate similarity values between image features of all image objects of a set of image objects, wherein the image features include average color, histogram color, positional color and texture; and a cluster generator, said cluster generator operative to generate at least one subset of image object identifiers based on a first comparison between a threshold criteria and each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of the set, wherein the threshold criteria is a fixed range of values designated by a user indicating a required degree of similarity between the image features of the image objects in a cluster, and, in response to determining that a similarity value between the first image object and a second image object meets the threshold criteria, a second comparison between the threshold criteria and each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set; wherein said cluster generator operative to logically group image object identifiers associated with the first, second, and third image objects into a cluster if a similarity value between the second image object and a third image object meets the threshold criteria.
Claim 12
Independent Claim
229 words
An apparatus, comprising: a similarity value generator, said similarity value generator operative to generate similarity values between image features of all image objects of a set of image objects, wherein the image features include average color, histogram color, positional color and texture; and a cluster generator, said cluster generator operative to generate at least one subset of image object identifiers based on a first comparison between a threshold criteria and each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of the set, wherein the threshold criteria is a fixed range of values designated by a user indicating a required degree of similarity between the image features of the image objects in a cluster, and, in response to determining that a similarity value between the first image object and a second image object meets the threshold criteria, a second comparison between the threshold criteria and each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set; wherein said cluster generator operative to logically group image object identifiers associated with the first, second, and third image objects into a cluster if a similarity value between the second image object and a third image object meets the threshold criteria.
Claim 13
Independent Claim
224 words
A method of identifying subsets of interrelated image objects from a set of image objects, comprising: comparing image features of each image object of the set with image features of all other image objects of the set, wherein the image features compared include average color, histogram color, positional color and texture; generating a similarity value for each comparison; comparing a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of a set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; in response to determining that a similarity value between the first image object and a second image object meets the threshold criteria, comparing the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set; and in response to determining that a similarity value between the second image object and a third image object meets the threshold criteria, logically grouping image object identifiers associated with the first, second, and third image objects into a cluster.
Claim 14
Independent Claim
224 words
A method of identifying subsets of interrelated image objects from a set of image objects, comprising: comparing image features of each image object of the set with image features of all other image objects of the set, wherein the image features compared include average color, histogram color, positional color and texture; generating a similarity value for each comparison; comparing a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of a set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; in response to determining that a similarity value between the first image object and a second image object meets the threshold criteria, comparing the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set; and in response to determining that a similarity value between the second image object and a third image object meets the threshold criteria, logically grouping image object identifiers associated with the first, second, and third image objects into a cluster.
Claim 15
Dependent Claim
239 words
A computer software product, comprising: similarity value generator code, said similarity value generator code executable to generate a similarity value for image features of each image object and all other image objects of a set of at least four image objects in response to a single request, wherein the image features include average color, histogram color, positional color and texture; cluster generator code, said cluster generator code executable to generate at least one cluster of image object identifiers, wherein the cluster generator code further includes: primary compare software, said primary compare software executable to compare a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of the set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; and secondary compare software, said secondary compare software, in response to determining from said primary compare software, that a similarity value between the first image object and a second image object meets the threshold criteria, said secondary compare software executable to compare the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set.
Claim 16
Dependent Claim
239 words
A computer software product, comprising: similarity value generator code, said similarity value generator code executable to generate a similarity value for image features of each image object and all other image objects of a set of at least four image objects in response to a single request, wherein the image features include average color, histogram color, positional color and texture; cluster generator code, said cluster generator code executable to generate at least one cluster of image object identifiers, wherein the cluster generator code further includes: primary compare software, said primary compare software executable to compare a threshold criteria designated by a user in a query with each one of a first plurality of similarity values, the first plurality of similarity values being between a first image object and other image objects of the set, wherein the threshold criteria is a fixed range of values designated by the user indicating a required degree of similarity between the image features of the image objects in a cluster; and secondary compare software, said secondary compare software, in response to determining from said primary compare software, that a similarity value between the first image object and a second image object meets the threshold criteria, said secondary compare software executable to compare the threshold criteria with each one of a second plurality of similarity values, the second plurality of similarity values being between the second image object and other image objects of the set.
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