Image interpretation method and apparatus
First Claim
Patent Images
1. A method of selecting a semantic label to describe a portion of an image, using a statistical or probabilistic interpretation model, said method comprising the steps of:
- providing one or more distinct semantic labels;
determining an initial probability value for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing contextual information simultaneously with image data representing the image, the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
analyzing the contextual information captured with the image data to identify predetermined features relating to the image;
adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features; and
analysing at least the portion of the image using the interpretation model in order to select at least one of the distinct semantic labels to describe the portion of the image, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image.
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Abstract
A method of interpreting an image using a statistical or probabilistic interpretation model is disclosed. The image has associated therewith contextual information. The method comprises the following steps: providing the contextual information associated with the image for analysis; analyzing the additional contextual information to identify predetermined features relating to the image; and biasing the statistical or probabilistic interpretation model in accordance with the identified features.
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Citations
32 Claims
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1. A method of selecting a semantic label to describe a portion of an image, using a statistical or probabilistic interpretation model, said method comprising the steps of:
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providing one or more distinct semantic labels;
determining an initial probability value for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing contextual information simultaneously with image data representing the image, the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
analyzing the contextual information captured with the image data to identify predetermined features relating to the image;
adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features; and
analysing at least the portion of the image using the interpretation model in order to select at least one of the distinct semantic labels to describe the portion of the image, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13)
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12. A method of selecting a semantic label to describe an object in an image, using a statistical or probabilistic interpretation model, said method comprising the steps of:
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providing one or more distinct semantic labels;
determining an initial probability value for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing contextual information simultaneously with image data representing the image, the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
analyzing the contextual information captured with the image data to identify predetermined features relating to the image;
adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features;
analyzing at least a portion of the image comprising the object using the statistical or probabilistic interpretation model; and
selecting at least one of the distinct semantic labels to describe the object in the image based on the analysis of the image, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image.
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14. An image interpretation system for selecting a semantic label to describe an object of an image, the image comprising a plurality of objects, the system comprising:
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initial probability determining means for determining an initial probability values for substantially each of one or more predetermined semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing means for capturing image data representing the image simultaneously with contextual information the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
analyzing means for analyzing the captured contextual information captured with the image data to identify predetermined features of the information;
initial probability adjusting means for adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features; and
image data analyzing means for analysing at least a portion of the image comprising the object using a statistical or probabilistic interpretation model in order to select at least one of the distinct semantic labels to describe the object in the image, wherein said at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image. - View Dependent Claims (15)
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16. A computer readable medium including instruction modules arranged to select a semantic label to describe an object in an image, using a statistical or probabilistic interpretation model, said modules comprising:
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an initial probability determining module for determining an initial probability value for substantially each of one or more predetermined semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
a capturing module for capturing image data representing the image simultaneously with contextual information, the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
an analyzing module for identifying, from the contextual information corresponding to the captured image data, predetermined features relating to the image;
initial probability adjusting module for adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features; and
an image data analyzing module for analysing at least a portion of the image comprising the object using the statistical or probabilistic interpretation model, in order to select at least one of the distinct semantic labels to describe the object, wherein said at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image. - View Dependent Claims (17)
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18. An apparatus for selecting a semantic label to describe a portion of an image using a statistical or probabilistic interpretation model, said apparatus comprising:
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means for providing one or more distinct semantic labels;
means for determining an initial probability value for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing means for capturing contextual information simultaneously with image data representing the image, the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
means for analyzing the contextual information captured with the image data to identify predetermined features relating to the image;
means for adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features; and
means for analysing at least the portion of the image using the interpretation model in order to select at least one of the distinct semantic labels to describe the portion of the image, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30)
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29. An apparatus for selecting a semantic label to describe an object in an image, using a statistical or probabilistic interpretation model the apparatus comprising:
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means for providing one or more distinct semantic labels;
means for determining an initial probability value for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
means for capturing contextual information simultaneously with image data representing the image, the contextual information being directly associated with the captured image data and originating from one or more different types of information sources, the information sources being distinct from the image and the captured image data representing the image;
means for analyzing the contextual information captured with the image data to identify predetermined features relating to the image;
means for adjusting the initial probability values for the one or more semantic labels based on the analysis of the captured contextual information and on the identified features;
means for analyzing at least a portion of the image data comprising the object using the statistical or probabilistic interpretation model; and
means for selecting at least one of the distinct semantic labels to describe the object in the image based on the image data analysis, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image.
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31. A method of selecting a semantic label to describe a portion of an image, using a statistical or probabilistic interpretation model, said method comprising the steps of:
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providing one or more distinct semantic labels;
determining an initial probability values for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing contextual information simultaneously with image data representing the image, the contextual information being directly associated with the captured image data and originating from one or more different types information sources, said information sources being distinct from the image and the captured image data representing the image;
analyzing the contextual information captured with the image data to identify predetermined features relating to the image;
selecting an application domain from a plurality of predetermined application domains based on the features identified, each of the predetermined application domains having a corresponding one or more of the semantic labels;
adjusting the initial probability values for the one or more semantic labels of the selected application domain based on the analysis of the contextual information and on the identified features; and
analysing at least the portion of the image using the interpretation model in order to select at least one of the distinct semantic labels to describe the portion of the image, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image.
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32. A method of selecting a semantic label to describe at least one portion of an image, using a statistical or probalistic interpretation model, said method comprising the steps of:
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providing one or more distinct semantic labels;
determining an initial probability value for substantially each of the one or more semantic labels, each of said initial probability values being set to a predetermined default value for each of the one or more semantic labels;
capturing audio data simultaneously with image data representing the image;
analyzing the audio data captured with the image data to identify predetermined features relating to the image;
adjusting the initial probability values for the one or more semantic labels based on the analysis of the audio data and on the identified features; and
analysing at least the portion of the image using the interpretation model in order to select at least one of the distinct semantic labels to describe the portion of the image, wherein the at least one semantic label is selected by multiplying the adjusted initial probability value for each semantic label with a conditional probability density value determined for the portion of the image.
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Specification