Method, system, storage medium, and data structure for image recognition using multilinear independent component analysis

• US 7,693,299 B2
• Filed: 01/13/2005
• Issued: 04/06/2010
• Est. Priority Date: 01/13/2004
• Status: Expired due to Fees

• Current Assignee: New York University
• Original Assignee: New York University
• Inventors: Terzopoulos, Demetri, Vasilescu, Manuela Alex O.
• Primary Examiner(s): BHATNAGAR, ANAND P
• Application Number:
  US11/571,341
• Publication Number:
  US 20080247608A1
• Time in Patent Office: 1,909 Days
• Field of Search: 382/100, 382232-253, 348/414, 348/417, 348/418, 348/422
• US Class Current: 382/100
• CPC Class Codes: G06F 18/21342 using statistical independence, i.e. minimising mutual information or maximising non-gaussianity
• CPC Class Codes: G06V 40/169 Holistic features and representations, i.e. based on the facial image taken as a whole

Abstract

A method, system, computer-readable medium and data structure are provided for processing image data in connection with image recognition. A response of an image (FIG. 6 element 210) to a basis tensor can be determined after the image is applied thereto. The image response can be flattened (FIG. 6 element 220). A coefficient vector may be extracted from the image response (FIG. 6 element 230). The extracted coefficient vector may be compared to a plurality of different parameters stored in coefficient rows of a matrix (FIG. 6 element 240).

Claims

1. A method of processing data, comprising:

using a computing arrangement, automatically applying image data associated with an image to a basis tensor;

obtaining response data based on the application of the image data to the basis tensor;

using the computing arrangement, automatically extracting a coefficient vector from the response data, and

at least one of displaying or storing the response data in a storage arrangement in at least one of a user-accessible format or a user-readable format.

2. The method of claim 1, wherein the response data relates to a multimodal response to factors that compose the image.

3. The method of claim 1, further comprising flattening an image section of the image corresponding to the response data.

4. The method of claim 3, wherein the flattening step comprises flattening the image section using a predetermined mode to arrange the response data in a further matrix.

5. The method of claim 3, wherein the flattening step comprises flattening the image section using a predetermined mode to arrange the response data in a further matrix having columns that are multiples of image parameters of the image.

6. The method of claim 3, wherein the flattening step comprises flattening the response data based on a person mode.

7. The method of claim 1, wherein the extracting step comprises extracting a person coefficient vector from the response data.

8. The method of claim 1, wherein the extracting step comprises extracting the coefficient vector using a singular value decomposition procedure.

9. The method of claim 1, further comprising comparing the extracted coefficient vector to a plurality of parameters provided in a matrix of further data associated with further images.

10. The method of claim 9, wherein the comparing step comprises comparing an extracted person coefficient vector to a plurality of people parameters stored in people coefficient rows of the matrix.

11. The method of claim 10, wherein the comparing step is performed using a cosine function that is approximately equivalent to a normalized nearest neighbor.

12. The method of claim 1, further comprising:

using the computing arrangement, automatically producing a representation of the image data having a plurality of sets of coefficients, wherein each of the sets is statistically independent from one another.

13. A computer system, comprising:

a processing arrangement capable of receiving data associated with an image; and

a storage provided in communication with the processing arrangement, and having stored thereon computer-executable set of instructions, wherein the set of instructions comprise;

i. a first set configured to enable the processing arrangement to apply image data associated with an image to a basis tensor;

ii. a second set configured to enable the processing arrangement to obtain response data based on the application of the image data to the basis tensor; and

iii. a third set configured to enable the processing arrangement to extract a coefficient vector from the response data.

14. The computer system of claim 13, wherein the response data relates to is a multimodal response to factors that compose the image.

15. The computer system of claim 13, wherein the set of instruction further comprises a fourth set configured to enable the processing arrangement to flatten an image section of the image corresponding to the response data.

16. The computer system of claim 15, wherein the fourth set is capable of configuring the processing arrangement to flatten the image section using a predetermined mode to arrange the response data in a further matrix.

17. The computer system of claim 15, wherein the image section is flattened using a predetermined mode to arrange the response data in a further matrix having columns that are multiples of image parameters of the image.

18. The computer system of claim 15, wherein the image section is flattened using based on a person mode.

19. The computer system of claim 13, wherein the third set configures the processing arrangement to extract a person coefficient vector from the response data.

20. The computer system of claim 13, wherein the third set configures the processing arrangement to extract the coefficient vector using a singular value decomposition procedure.

21. The computer system of claim 13, wherein the set of instruction further comprises a fifth set configured to enable the processing arrangement to comparing the extracted coefficient vector to a plurality of parameters provided in a matrix of further data associated with further images.

22. The computer system of claim 21, wherein the fourth set configures the processing arrangement to compare an extracted person coefficient vector to a plurality of people parameters stored in people coefficient rows of the matrix.

23. The computer system of claim 22, wherein the comparison is performed using a cosine function that is approximately equivalent to a normalized nearest neighbor.

24. A computer-readable medium having stored thereon computer-executable set of instructions, the set of instructions comprising:

i. a first set configured to enable a processing arrangement to apply image data associated with an image to a basis tensor;

ii. a second set configured to enable the processing arrangement to obtain response data based on the application of the image data to the basis tensor; and

iii. a third set configured to enable the processing arrangement to extract a coefficient vector from the response data.