NEAR-DUPLICATE IMAGE DETECTION

• US 20120076423A1
• Filed: 09/27/2010
• Published: 03/29/2012
• Est. Priority Date: 09/27/2010
• Status: Active Grant

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Tang, Feng, Lim, Suk Hwan, Gao, Yuli
• Granted Patent: US 8,792,728 B2
• Time in Patent Office: Days
• US Class Current: 382/203
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06V 10/757 Matching configurations of points or features

Abstract

A system and a method for near-duplicate image detection performed by a physical computing system includes applying a feature determining function to a number of images, a feature being defined by a geometric shape, comparing characteristics of said geometric shapes defining said features from at least two of said number of images, and characterizing said at least two of said number of images as a near-duplicate match if a predetermined percentage of said features of said at least two images match.

Claims

1. A method for near-duplicate image detection performed by a physical computing system, the method comprising:

applying a feature detection function to a number of images, a feature being defined by a geometric shape;

comparing characteristics of said geometric shapes defining said features from at least two of said number of images; and

characterizing said at least two of said number of images as a near-duplicate match if a predetermined percentage of said features of said at least two images match.

2. The method of claim 1, in which said geometric shape comprises an ellipse.

3. The method of claim 2, in which said characteristics of said geometric shapes comprise at least one of:

a horizontal coordinate, a vertical coordinate, a major axis, a minor axis, and an angle or rotation.

4. The method of claim 1, further comprising using a Gaussian distribution to determine a tolerance range, said tolerance range used to determine if said corresponding features from said at least two images match.

5. The method of claim 4, in which a standard deviation used by said Gaussian distribution is a value of a characteristic of said geometric shape multiplied by a coefficient.

6. The method of claim 5, further comprising adjusting said constant to adjust said tolerance range.

7. The method of claim 1, further comprising determining if said features from said at least two images match by determining if neighboring features of said features from said at least two images match.

8. A physical computing system comprising:

a processor; and

a memory communicatively coupled to said processor;

in which said processor is configured to;

apply a feature detection function to a number of images, a feature being defined by a geometric shape;

compare characteristics of said geometric shapes; and

characterize at least two of said number of images as near-duplicate matches if a predetermined percentage of said features of said at least two of said number of images match.

9. The system of claim 8, in which said geometric shape comprises an ellipse.

10. The system of claim 9, in which said characteristics of said geometric shapes comprise at least one of:

a horizontal coordinate, a vertical coordinate, a major axis, a minor axis, and an angle or rotation.

11. The system of claim 8, in which said processor is further to use a Gaussian distribution to determine a tolerance range, said tolerance range used to determine if a feature from one of said number of images matches a feature from another of said number of images.

12. The system of claim 11, in which a standard deviation used by said Gaussian distribution is a value of a characteristic of said geometric shape multiplied by a constant.

13. The system of claim 12, in which said processor is further to adjust said constant to adjust said tolerance range.

14. The system of claim 8, in which said processor is further to determine if a feature from one of said number of images matches a feature from another of said number of images matches by determining if neighboring features of said features match.

15. A method for near-duplicate image detection performed by a physical computing system, the method comprising:

applying a feature detection function to a number of images, a feature being defined by an ellipse;

comparing characteristics of said ellipse; and

characterizing at least two of said number of images as near-duplicate matches if a predetermined percentage of said features of said at least two of said number of images match;

in which features are classified as a match if values of said characteristics of said ellipses are within a predefined tolerance range.