DETECTING RECURRING EVENTS IN CONSUMER IMAGE COLLECTIONS

• US 20120051644A1
• Filed: 08/25/2010
• Published: 03/01/2012
• Est. Priority Date: 08/25/2010
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Das, Madirakshi, Loui, Alexander C.
• Granted Patent: US 8,634,662 B2
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 18/2321 using statistics or function optimisation, e.g. modelling of probability density functions
• CPC Class Codes: G06V 20/30 in albums, collections or shared content, e.g. social network photos or video
• CPC Class Codes: G06V 20/35 Categorising the entire scene, e.g. birthday party or wedding scene

Abstract

A method of detecting recurring events in a digital image collection taken over a pre-determined period of time is disclosed. The method uses a processor for analyzing the digital image collection to produce a two-dimensional representation of the distribution of image capture activity over time and detecting recurring events by identifying spatial clusters in the two-dimensional representation.

Claims

1. A method of detecting recurring events in a digital image collection taken over a pre-determined period of time comprising, using a processor for,

(a) analyzing the digital image collection to produce a multi-dimensional representation of the distribution of image capture activity over time;

and

(b) detecting recurring events by identifying spatial clusters in the multi-dimensional representation.

2. The method of claim 1 wherein the detection of recurring events is further refined by analyzing the commonality of features between events.

3. The method of claim 1 wherein the digital image collection wherein the pre-determined period of time includes multiple years and step (b) includes identifying spatial clusters that span the multiple years.

4. The method of claim 1 wherein the digital image collection wherein the pre-determined period of time includes a year or less and step (b) includes identifying spatial clusters that occur within the pre-determined period of time.

5. The method of claim 1 wherein the multi-dimensional representation of the digital image capture activity is based on representing events on a day-of-the-year axis and a year axis.

6. The method of claim 1 wherein the multi-dimensional representation of the digital image capture activity is based on representing events on a week-of-the-year axis and a day-of-the-week axis.

7. The method of claim 2 wherein the analysis of the commonality of features includes content based analysis or event based analysis.

8. The method of claim 7 wherein the content based analysis includes using people recognition, scene classification, or object identification.

9. The method of claim 7 wherein the event based analysis includes event classification, location matching, or time based features.

10. The method of claim 1 wherein the detected recurring events are displayed to a user.

11. A method of organizing a digital image collection by detecting recurring events in a digital image collection taken over a pre-determined period of time comprising, using a processor for:

(a) analyzing the digital image collection to produce a multi-dimensional representation of the distribution of image capture activity over time;

(b) detecting recurring events by identifying spatial clusters in the multi-dimensional representation; and

(c) indexing images based on the detected recurring events.

12. The method of claim 11 wherein the detection of recurring events is further refined by analysis of the commonality of features between events.

13. The method of claim 11 wherein the digital image collection wherein the pre-determined period of time includes multiple years and step (b) includes identifying spatial clusters that span the multiple years.

14. The method of claim 11 wherein the digital image collection wherein the pre-determined period of time includes a year or less and step (b) includes identifying spatial clusters that occur within the pre-determined period of time.

15. The method of claim 11 wherein the two-dimensional representation of the digital image capture activity is based on representing events on a day-of-the-year axis and a year axis.

16. The method of claim 11 wherein the multi-dimensional representation of the digital image capture activity is based on representing events on a week-of-the-year axis and a day-of-the-week axis.

17. The method of claim 12 wherein the analysis of the commonality of features includes content based analysis or event based analysis.

18. The method of claim 17 wherein the content based analysis includes using people recognition, scene classification, or object identification.

19. The method of claim 17 wherein the event based analysis includes event recognition, location matching, or time based features.

20. The method of claim 11 wherein the organized digital image collection is displayed to the user.