Robust recognizer of perceptually similar content

• US 6,996,273 B2
• Filed: 04/24/2001
• Issued: 02/07/2006
• Est. Priority Date: 04/24/2001
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Mihcak, M. Kivanc, Venkatesan, Ramarathnam
• Primary Examiner(s): Bali, Vikkram
• Assistant Examiner(s): LAROSE, COLIN M
• Application Number:
  US09/843,234
• Publication Number:
  US 20020196976A1
• Time in Patent Office: 1,750 Days
• Field of Search: 382/181, 382/190, 382/201, 382/206, 382/207, 382/224
• US Class Current: 382/181
• CPC Class Codes: G06F 18/00 Pattern recognition

Abstract

An implementation of a technology is described herein for recognizing the perceptual similarity of the content of digital goods. At least one implementation, described herein, introduces a new hashing technique. More particularly, this hashing technique produces hash values for digital goods that are proximally near each other, when the digital goods contain perceptually similar content. In other words, if the content of digital goods are perceptually similar, then their hash values are, likewise, similar. The hash values are proximally near each other. This is unlike conventional hashing techniques where the hash values of goods with perceptually similar content are far apart with high probability in some distance sense (e.g., Hamming). This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

Claims

1. A method facilitating similarity recognition of a digital signal, the method comprising:

obtaining a digital signal; and

deriving a recognition value representative of the digital signal such that perceptually distinct digital signals result in recognition values that are approximately independent of one another and perceptually similar digital signals result in proximally similar recognition values, wherein deriving a recognition value includes;

transforming a digital signal into a digital signal transform;

quantizing the digital signal transform;

geometric-region-growing the digital signal transform;

generating the recognition value of the digital signal.

2. A method as recited in claim 1 further comprising:

pseudorandomly segmenting the digital signal into one or more segments;

for one or more of the segments, repeating the transforming, the quantizing, and the geometric-region-growing, wherein these repeated tasks are performed on a segment rather than the entire signal;

combining one or more of the segments.

3. A method as recited in claim 1 further comprising comparing the recognition value with another recognition value derived from another digital signal.

4. A method as recited in claim 1, wherein the recognition value is a hash value.

5. A method as recited in claim 1, wherein the digital signals are digital image signals.

6. One or more computer-readable media having computer-executable instructions embodied therein, that, when executed by one or more processors, cause the one or more processors to perform acts comprising:

obtaining a digital signal; and

deriving a recognition value representative of the digital signal such that perceptually distinct digital signals result in recognition values that are approximately independent of one another and perceptually similar digital signals result in proximally similar recognition values, wherein deriving a recognition value comprises;

transforming a digital signal into a digital signal transform;

quantizing the digital signal transform;

geometric-region-growing the digital signal transform;

generating the recognition value of the digital signal.

7. One or more media as recited in claim 6 further comprising computer-executable instructions configured to cause the one or more processors to perform acts comprising:

pseudorandomly segmenting the digital signal into one or more segments;

for one or more of the segments, repeating the transforming, the quantizing, and the geometric-region-growing, wherein these repeated tasks are performed on a segment rather than the entire signal;

combining one or more of the segments.

8. A computer configured to perform acts comprising:

obtaining a digital signal; and

deriving a recognition value representative of the digital signal such that perceptually distinct digital signals result in recognition values that are approximately independent of one another and perceptually similar digital signals result in proximally similar recognition values, wherein deriving a recognition value comprising;

transforming a digital signal into a digital signal transform;

quantizing the digital signal transform;

geometric-region-growing the digital signal transform;

generating the recognition value of the digital signal.

9. A computer as recited in claim 8, the acts further comprising:

pseudorandomly segmenting the digital signal into one or more segments;

for one or more of the segments, repeating the transforming, the quantizing, and the geometric-region-growing, wherein these repeated tasks are performed on a segment rather than the entire signal;

combining one or more of the segments.