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EP0990996A2
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A method for automatically classifying images into events
Assignee: Eastman Kodak Co
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Filing: 1999-09-17
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Granted: —
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Expires: 2019-09-17
Abstract
Abstract A method for automatically classifying images into events, the method includes the steps of: receiving a plurality of images having either or both date and/or time of image capture; determining one or more largest time differences of the plurality of images based on clustering of the images; and separating the plurality of images into the events based on having one or more boundaries between events which one or more boundaries correspond to the one or more largest time differences.
Inventors
- 👤 Alexander Cheng Pong Eastman Kodak Company Loui
- 👤 Eric Serge Eastman Kodak Company Pavie
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-11-09 14:08
CPC Classifications
G; G06; G06F; G06F16/00; G06F16/50; G06F16/58; G06F16/583; G06F16/5838; G06F18/00; G06F18/20; G06F18/23; G06V; G06V10/00; G06V10/40; G06V10/50; G06V10/507; G06V10/70; G06V10/74; G06V10/75; G06V10/758; G06V10/762; G06V20/00; G06V2201/00; G06V2201/10
Claims Analysis
Total Claims: 10
Claim 1
Independent Claim
82 words
A method for automatically classifying images into events, the
method comprising the steps of: (a) receiving a plurality of images having either or both date and/or
time of image capture; (b) determining one or more largest time differences of the
plurality of images based on clustering of the images; and, (c) separating the plurality of images into the events based on
having one or more boundaries between events which one or more boundaries
correspond to the one or more largest time differences.
Claim 2
Dependent Claim
32 words
The method as in claim 1, wherein step (b) includes computing
a time difference histogram and performing a 2-means clustering on the time
difference histogram for defining the one or more boundaries.
Claim 3
Dependent Claim
27 words
The method as in claim 2, wherein step (b) further includes
mapping the time difference histogram through a time difference scaling function
before performing the 2-means clustering.
Claim 4
Dependent Claim
27 words
The method as in claim 2, wherein step (c) includes checking
the images adjacent the one or more boundaries for similarity by comparing
content of the images.
Claim 5
Dependent Claim
28 words
The method as in claim 4, wherein step (c) includes checking
the images adjacent the one or more boundaries for similarity by using a block-based
histogram correlation technique.
Claim 6
Dependent Claim
22 words
The method as in claim 5 further comprising step (d) dividing
the events into subject grouping by using an image content analysis.
Claim 7
Dependent Claim
22 words
The method as in claim 6, wherein step (d) includes dividing the
events into subject grouping by using a block-based histogram technique.
Claim 8
Independent Claim
67 words
A method for automatically classifying images into events, the
method comprising the steps of: (a) receiving a plurality of images arranged in chronological order; (b) dividing the images into a plurality of blocks; and, (c) grouping the images into subject grouping based on block-based
histogram correlation which includes computing a color histogram of each
block and computing a histogram intersection value which determines the
similarity between blocks.
Claim 9
Dependent Claim
38 words
The method as in claim 8, wherein step (c) includes
comparisons of two of the images by shifting one of the images in a desired
direction based on the intersection value and then computing the block based
correlation.
Claim 10
Dependent Claim
38 words
The method as in claim 9, wherein step (c) includes forming a
map that contains two best intersection values of each of the block comparisons;
dividing the map into three portions; and then comparing center portions for
similarity.
References Cited (Backward Citations)
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