Computer implemented technique for analyzing images

• US 8,233,702 B2
• Filed: 08/20/2007
• Issued: 07/31/2012
• Est. Priority Date: 08/18/2006
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Gokturk, Salih Burak, Torresani, Lorenzo, Lee, Kuang-chih, Anguelov, Dragomir
• Primary Examiner(s): Bella, Matthew
• Assistant Examiner(s): TORRES, JOSE
• Application Number:
  US11/841,355
• Publication Number:
  US 20080199075A1
• Time in Patent Office: 1,807 Days
• Field of Search: 382118-160, 700 28- 49
• US Class Current: 382/155
• CPC Class Codes: G06F 18/2137 based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
• CPC Class Codes: G06F 18/24147 Distances to closest patterns, e.g. nearest neighbour classification
• CPC Class Codes: G06V 10/7715 Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
• CPC Class Codes: G06V 40/169 Holistic features and representations, i.e. based on the facial image taken as a whole
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

An input image is received, represented by a matrix D having a first number of dimensions. Each of the first number of dimensions may represent or correspond to a portion of the image. A metric objective may be identified. A dimensional reduction on the matrix D may then be performed that optimize the metric objective, so that a matrix d of a second number of dimensions is identified to represent the input image, where the second number of dimensions is less than the first number of dimensions.

Claims

1. A method for performing at least a portion of an image analysis process, the method being implemented by one or more processors and comprising:

receiving an input image represented by a matrix D having a first number of dimensions, wherein each of the first number of dimensions represents a portion of the image;

identifying a metric objective that is related to a desired classification result of performing the image analysis process; and

performing a dimensional reduction on the matrix D that optimizes the metric objective, so that a matrix d of a second number of dimensions is identified to represent the input image, wherein the second number of dimensions is less than the first number of dimensions.

2. The method of claim 1, wherein performing a dimensional reduction on the matrix D includes solving for the matrix d using a Euclidian distance in a space that minimizes a metric objective.

3. The method of claim 1, further comprising identifying a nonlinear feature space in the input image, and programmatically learning Mahalanobis distance metrics in nonlinear feature spaces.

4. The method of claim 3, wherein performing a dimensional reduction on the matrix D includes determining a kernalized version of the metric objective, including learning the metric objective.

5. The method of claim 1, wherein performing a dimensional reduction includes using a transformation matrix L that is nonsquare and of a size of d×

D, wherein d is less than D.

6. The method of claim 1, wherein receiving an input image includes receiving a face as part of a facial recognition process.

7. A computer storage medium that stores computer executable instructions for performing at least a portion of an image analysis process, the computer executable instructions including instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

receiving an input image represented by a matrix D having a first number of dimensions, wherein each of the first number of dimensions represents a portion of the image;

identifying a metric objective that is related to a desired classification result of performing the image analysis process; and

performing a dimensional reduction on the matrix D that optimizes the metric objective, so that a matrix d of a second number of dimensions is identified to represent the input image, wherein the second number of dimensions is less than the first number of dimensions.

8. The computer storage medium of claim 7, wherein the instructions for performing the dimensional reduction include instructions for solving for the matrix d using a Euclidian distance in a space that minimizes a metric objective.

9. The computer storage medium of claim 7, further comprising instructions for identifying a nonlinear feature space in the input image, and programmatically learning Mahalanobis distance metrics in nonlinear feature spaces.

10. The computer storage medium of claim 7, wherein the instructions for performing the dimensional reduction include instructions for determining a kernalized version of the metric objective, including learning the metric objective.

11. The computer storage medium of claim 7, wherein the instructions for performing the dimensional reduction include instructions for using a transformation matrix L that is nonsquare and of a size of d×

D, wherein d is less than D.

12. The computer storage medium of claim 7, wherein the instructions for receiving the input image include instructions for receiving a face as part of a facial recognition process.

13. An image analysis system, comprising:

a memory that stores instructions for performing at least a portion of an image analysis process; and

one or more processors configured to;

receive an input image represented by a matrix D having a first number of dimensions, wherein each of the first number of dimensions represents a portion of the image;

identify a metric objective that is related to a desired classification result of performing the image analysis process; and

perform a dimensional reduction on the matrix D that optimizes the metric objective, so that a matrix d of a second number of dimensions is identified to represent the input image, wherein the second number of dimensions is less than the first number of dimensions.

14. The image analysis system of claim 13, wherein the one or more processors are configured to perform the dimensional reduction by solving for the matrix d using a Euclidian distance in a space that minimizes a metric objective.

15. The image analysis system of claim 13, wherein the one or more processors are configured to identify a nonlinear feature space in the input image, and programmatically learning Mahalanobis distance metrics in nonlinear feature spaces.

16. The image analysis system of claim 13, wherein the one or more processors are configured to perform the dimensional reduction by determining a kernalized version of the metric objective, including learning the metric objective.

17. The image analysis system of claim 13, wherein the one or more processors are configured to perform the dimensional reduction by using a transformation matrix L that is nonsquare and of a size of d×

D, wherein d is less than D.

18. The image analysis system of claim 13, wherein the one or more processors are configured to receive the input image by receiving a face as part of a facial recognition process.