IMAGE INTERPRETATION METHOD AND APPARATUS

• US 20020034330A1
• Filed: 10/29/1998
• Published: 03/21/2002
• Est. Priority Date: 10/29/1997
• Status: Active Grant

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Information Systems Research Australia Pty Limited (Canon Inc.), Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: LENNON, ALISON JOAN, LE, DELPHINE ANH DAO
• Granted Patent: US 6,922,489 B2
• Time in Patent Office: Days
• US Class Current: 382/228
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06V 10/75 Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries

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.

Claims

1. A method of interpreting an image using a statistical or probabilistic interpretation model, the image having associated therewith contextual information, said method comprising the steps of:

providing the contextual information associated with the image for analysis;

analysing 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.

2. The method as recited in claim 1, further including the step of:

interpreting the image in accordance with the biased statistical or probabilistic interpretation model.

3. The method as recited in claim 2, further including the step of:

providing metadata describing at least a portion of said image.

4. The method as recited in claim 3, wherein said metadata includes labels describing at least a portion of the content of the image data.

5. The method as recited in claim 3, wherein said metadata is an encoded representation providing information of the content of at least a portion of the content of the image data.

6. The method as recited in claim 4, wherein said metadata includes information on an adjacency of regions of the image data.

7. The method as recited in claim 3, wherein said metadata is labelled Region Adjacency Graph (RAG).

8. The method as recited in any ne of claims 2 to 6 wherein said metadata is associated with the image data.

9. The method as recited in claim 1, wherein the contextual information is derived from a plurality of separate sources of information.

10. The method as recited in claim 1 or 6, wherein the contextual information originated from at least one of the following sources of information:

audio information;

electromagnetic spectral information;

environmental information;

time lapse or sequential image information; and

textual annotation, phrases or keywords associated with the image.

11. The method as recited in claim 1, wherein the biasing step includes selecting a predetermined application domain from a plurality of application domains based on the features identified.

12. The method as recited in claim 1, wherein the biasing step comprises biasing a probability value of at least one label associated with a predetermined application domain.

14. The method as recited in any one of the preceding claims, wherein the statistical or probabilistic interpretation model is a Markov Random Field Model.

16. The image interpretation system recited in claim 15, further comprising labelling means for providing semantic labels for each object identified.

18. The computer readable medium recited in claim 17, further including a labelling module for providing metadata describing the identified objects of the image.

20. The apparatus as recited in claim 19, further including:

means for interpreting the image in accordance with the biased statistical or probabilistic interpretation model.

21. The apparatus as recited in claim 20, further including:

means for providing metadata describing at least a portion of said image.

22. The apparatus as recited in claim 21, wherein said metadata includes labels describing at least a portion of the content of the image data.

23. The apparatus as recited in claim 21, wherein said metadata is an encoded representation providing information of the content of at least a portion of the content of the image data.

24. The apparatus as recited in claim 22, wherein said metadata includes information on an adjacency of regions of the image data.

25. The apparatus as recited in claim 21, wherein said metadata is a labelled Region Adjacency Graph (RAG).

26. The apparatus as recited in any one of claims 20 to 24 wherein said metadata is associated with the image data.

27. The apparatus as recited in claim 19, wherein the contextual information is derived from a plurality of separate sources of information.

28. The apparatus as recited in claim 19 or 24, wherein the contextual information originated from at least one of the following sources of information:

audio information;

electromagnetic spectral information;

environmental information;

time lapse or sequential image information; and

textual annotation, phrases or keywords associated with the image.

29. The apparatus as recited in claim 19, wherein the means for biasing includes a means for selecting a predetermined application domain from a plurality of application domains based on the features identified.

30. The apparatus as recited in claim 19, wherein the means for biasing comprises means for biasing a probability value of at least one label associated with a predetermined application domain.

13. A method of interpreting an image using a statistical or probabilistic interpretation model to identify at least one object in the image, the image having associated therewith contextual information, said method comprising the steps of:

providing contextual information associated with the image for analysis;

analysing the contextual information to identify predetermined features relating to the image associated with the additional contextual information;

biasing the statistical or probabilistic interpretation model in accordance with the identified features;

analysing the image in accordance with the biasing statistical or probabilistic interpretation model; and

providing metadata identifying or describing the at least one object in the image.

15. An image interpretation system for identifying at least one object of an image, the image comprising a plurality of objects, the system comprising:

input means for inputting image data and associated contextual information;

analysing means for analysing the contextual information to identify predetermined features of the information;

parameter adjusting means for biasing a statistical or probabilistic interpretation model in accordance with the identified predetermined features; and

image data analysing means based on the biased statistical or probabilistic interpretation model for identifying at least one object in the image.

17. A computer readable medium including instruction modules arranged to identify at least one object in an image, said modules comprising:

an analysing module provided for identifying from the contextual information predetermined features relating to the image;

a biasing module for altering a statistical or probabilistic interpretation model in accordance with the identified features;

an image analysing module for identifying in accordance with the biasing statistical or probabilistic interpretation model objects of the image.

19. An apparatus for interpreting an image using a statistical or probabilistic interpretation model, the image having associated therewith contextual information, said apparatus comprising:

means for providing the contextual information associated with the image for analysis;

means for analysing the additional contextual information to identify predetermined features relating to the image; and

means for biasing the statistical or probabilistic interpretation model in accordance with the identified features.

32. The apparatus as recited in any one of claims 19 to 31, wherein the statistical or probabilistic interpretation model is a Markov Random Field Model.

31. An apparatus for interpreting an image using a statistical or probabilistic interpretation model to identify at least one object in the image, the image having associated therewith contextual information, said apparatus comprising:

means for providing contextual information associated with the image for analysis;

means for analysing the contextual information to identify predetermined features relating to the image associated with the additional contextual information;

means for biasing the statistical or probabilistic interpretation model in accordance with the identified features;

means for analysing the image in accordance with the biasing statistical or probabilistic interpretation model; and

means for providing metadata identifying or describing the at least one object in the image.