EP1017019A2
Expired / Lapsed
EP
⏱️ 0.0 years remaining
Method for automatic determination of main subjects in photographic images
Assignee: Intellectual Ventures Fund 83 LLC
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Filing: 1999-12-03
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Granted: —
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Expires: 2019-12-03
Abstract
Abstract A method for detecting a main subject in an image, the method comprises: receiving a digital image; extracting regions of arbitrary shape and size defined by actual objects from the digital image; grouping the regions into larger segments corresponding to physically coherent objects; extracting for each of the regions at least one structural saliency feature and at least one semantic saliency feature; and integrating saliency features using a probabilistic reasoning engine into an estimate of a belief that each region is the main subject.
Inventors
- 👤 Jiebo c/o Eastman Kodak Company Luo
- 👤 Stephen c/o Eastman Kodak Company Etz
- 👤 Amit c/o Eastman Kodak Company Singhal
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-11-09 10:51
CPC Classifications
G; G06; G06T; G06T11/00; G06T11/60; G06F; G06F18/00; G06F18/20; G06F18/29; G06T7/00; G06T7/10; G06T7/11; G06T7/143; G06T7/194; G06V; G06V10/00; G06V10/20; G06V10/25; G06V10/40; G06V10/46; G06V10/462; G06V10/70; G06V10/84; G06V20/00; G06V20/10; G06V40/00; G06V40/10; G06V40/16; G06V40/161; G06V40/162
Claims Analysis
Total Claims: 10
Claim 1
Independent Claim
87 words
A method for detecting a main subject in an image, the
method comprising the steps of: a) receiving a digital image; b) extracting regions of arbitrary shape and size defined by actual
objects from the digital image; c) extracting for each of the regions at least one structural saliency
feature and at least one semantic saliency feature ; and, d) integrating the structural saliency feature and the semantic
feature using a probabilistic reasoning engine into an estimate of a belief that each
region is the main subject.
Claim 2
Dependent Claim
31 words
The method as in claim 1, wherein step (b) includes using a
color distance metric defined in a color space, a spatial homogeneity constraint,
and a mechanism for permitting spatial adaptivity.
Claim 3
Dependent Claim
33 words
The method as in claim 1, wherein step (c) includes using
either individually or in combination at least one low-level vision feature and at
least one geometric feature as the structural saliency feature.
Claim 4
Dependent Claim
57 words
The method as in claim 1 ,wherein step (c) includes using
either individually or in combination a color, brightness and/or texture as a low-level
vision feature; a location, size, shape, convexity, aspect ratio, symmetry,
borderness, surroundedness and/or occlusion as a geometric feature; and a flesh,
face, sky, grass and/or other green vegetation as the semantic saliency feature.
Claim 5
Dependent Claim
30 words
The method as in claim 1, wherein step (d) includes using a
collection of human opinions to train the reasoning engine to recognize the
relative importance of the saliency features.
Claim 6
Dependent Claim
29 words
The method as in claim 1, wherein step (c) includes using
either individually or in combination a self-saliency feature and a relative saliency
feature as the structural saliency feature.
Claim 7
Dependent Claim
18 words
The method as in claim 1, wherein step (d) includes using a
Bayes net as the reasoning engine.
Claim 8
Dependent Claim
30 words
The method as in claim 1, wherein step (d) includes using a
conditional probability matrix that is determined by using fractional frequency
counting according to a collection of training data.
Claim 9
Independent Claim
98 words
A method for detecting a main subject in an image, the
method comprising the steps of: a) receiving a digital image; b) extracting regions of arbitrary shape and size defined by actual
objects from the digital image; c) grouping the regions into larger segments corresponding to
physically coherent objects; d) extracting for each of the regions at least one structural saliency
feature and at least one semantic saliency feature; and, e) integrating the structural saliency feature and the semantic
feature using a probabilistic reasoning engine into an estimate of a belief that each
region is the main subject.
Claim 10
Dependent Claim
21 words
The method as in claim 9, wherein step (c) includes using
either individually or in combination non-purposive grouping and purposive
grouping.
References Cited (Backward Citations)
No citation records indexed.
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