Digital goods representation based upon matrix invariances

• US 7,831,832 B2
• Filed: 01/06/2004
• Issued: 11/09/2010
• Est. Priority Date: 01/06/2004
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Kozat, S. Serdar, Venkatesan, Ramarathnam, Mihcak, M. Kivanc
• Primary Examiner(s): Simitoski; Michael J
• Assistant Examiner(s): Jackson; Jenise E
• Application Number:
  US10/752,268
• Publication Number:
  US 20050149727A1
• Time in Patent Office: 2,499 Days
• Field of Search: 713/176
• US Class Current: 713/176
• CPC Class Codes: G06F 16/41 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06V 10/431 Frequency domain transformation; Autocorrelation
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit

Abstract

Described herein is an implementation that produces a new representation of a digital good (such as an image) in a new defined representation domain. In particular, the representations in this new domain are based upon matrix invariances. In some implementations, the matrix invariances may, for example, heavily use singular value decomposition (SVD).

Claims

1. A method for producing a new representation of a digital good in a new defined representation domain, the new defined representation domain based upon matrix invariances, the method comprising:

obtaining a digital goods;

selecting, by a partitioner in a computing device, a plurality of pseudo-randomly sized and pseudo-randomly positioned regions from the digital goods;

extracting robust features from the plurality of pseudo-randomly sized and pseudo-randomly positioned regions, wherein the features are based upon singular value decomposition (SVD), discrete cosine transform, or discrete wavelet transform and further wherein the features are within the new defined representation domain;

producing a first output comprising the calculated statistics of one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions, wherein the statistics of the one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions are representatives of respective one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions and are calculated based upon matrix invariances;

constructing a secondary representation of the digital goods by using a pseudo-random combination of the calculated statistics;

forming multiple second regions from the secondary representation;

generating a new set of feature vectors from each second region via a SVD transformation; and

producing a second output.

2. A method as recited in claim 1, wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.

3. A non-transitory method as recited in claim 1, wherein at least some of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions overlap.

4. A method as recited in claim 1, wherein the plurality of pseudo-randomly sized and pseudo-randomly positioned regions are not allowed to overlap with one another.

12. A computing device comprising:

an audio/visual output, which is configured to produce output that is audio, visual, or both;

a non-transitory medium as recited in claim 1.

17. A non-transitory medium as recited in claim 1, wherein pseudo-randomly selecting a plurality of regions comprises selecting a plurality of pseudo-randomly sized and pseudo-randomly positioned regions.

18. A non-transitory medium as recited in claim 1, wherein at least some of the plurality of regions overlap.

19. A non-transitory medium as recited in claim 1, wherein the plurality of regions are not allowed to overlap with one another.

5. A system comprising:

a processor;

one or more memories having stored therein processor-executable modules, the modules comprising;

an obtainer configured to obtain a digital goods;

a partitioner configured to pseudo-randomly partition the digital goods into a plurality of regions based at least upon a secret key;

a calculator configured to calculate singular vectors for one or more of the plurality of regions via singular value decompositions, wherein the singular vectors remain invariant with high probability for disturbances to the digital goods in a probability space defined by the secret key.

6. A system as recited in claim 5, wherein at least some of the plurality of regions overlap.

7. A system as recited in claim 5, wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.

8. A system as recited in claim 5, wherein the plurality of regions comprise a plurality of pseudo-sized and pseudo-positioned regions.

9. A non-transitory computer storage medium having processor-executable instructions that, when executed by a processor, performs a method comprising:

obtaining a digital goods;

pseudo-randomly partitioning the digital goods into a plurality of regions;

generating singular vectors for one or more of the plurality of regions via singular value decompositions, wherein the singular vectors remain invariant with high probability for disturbances to the digital goods.

10. A non-transitory medium as recited in claim 9, wherein the method further comprises extracting robust pseudo-random features of the digital goods, wherein the features are within a defined representation domain.

11. A non-transitory medium as recited in claim 9, wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.

13. A non-transitory computer storage medium having processor-executable instructions that, when executed by a processor, performs a method facilitating protection of digital goods, the method comprising:

obtaining a digital goods;

partitioning the digital goods into a plurality of pseudo-randomly sized and pseudo-randomly positioned regions, after the partitioning the digital goods, calculating statistics of one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions, wherein the statistics of the one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions are representatives of respective one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions and are calculated based upon matrix invariances;

producing output comprising the calculated statistics of the one or more of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions.

14. A non-transitory medium as recited in claim 13, wherein at least some of the plurality of pseudo-randomly sized and pseudo-randomly positioned regions overlap.

15. A non-transitory medium as recited in claim 13, wherein the matrix invariances include singular value decomposition (SVD).

16. A non-transitory medium as recited in claim 13, wherein the digital goods is selected from a group consisting of a digital image, a digital audio clip, a digital video, a database, and a software image.

20. A non-transitory medium as recited in claim 13, wherein prior to the partitioning, the method further comprises normalizing amplitudes of the digital goods.