초록 ▼

A method of establishing a connection between two image collections, the method includes providing a first image collection from a first user; providing a second image collection from a different second user; analyzing the first and second image collections to produce a similarity score between the

A method of establishing a connection between two image collections, the method includes providing a first image collection from a first user; providing a second image collection from a different second user; analyzing the first and second image collections to produce a similarity score between the first and second image collections; and establishing a link for sharing images or collection data between the first and second users based on the similarity score.

대표청구항 ▼

The invention claimed is: 1. A method of establishing a connection between two image collections, comprising a collection controller for performing the following steps: a) providing a first image collection from a first user; b) providing a second image collection from a different second user; c) a

The invention claimed is: 1. A method of establishing a connection between two image collections, comprising a collection controller for performing the following steps: a) providing a first image collection from a first user; b) providing a second image collection from a different second user; c) analyzing the first and second image collections to produce a similarity score between the first and second image collections; and d) establishing a link for sharing images or collection data between the first and second users based on the similarity score. 2. The method of claim 1, wherein step d) includes one of the users accessing both image collections or both of the users accessing both image collections. 3. The method of claim 1, wherein step c) includes: i) detecting unique objects from the first and the second image collections; ii) determining the likelihood that a unique object from the first image collection also appears in the second image collection; and iii) producing the similarity score based on the determined likelihood. 4. The method of claim 3, wherein step c) includes producing a weight based on the type of unique object that is detected and using the weight and the likelihood to produce the similarity score. 5. The method of claim 1, wherein step c) includes using the image capture time or the image capture location from at least one image from at least one image collection to produce the similarity score. 6. The method of claim 1, wherein step c) further includes using a label from at least one of the image collections. 7. The method of claim 1, wherein the label is a name associated with a person. 8. The method of claim 3, wherein step cii) includes using the image capture time or the image capture location from at least one image from at least one image collection. 9. The method of claim 8, wherein step cii) includes computing a time difference between at least one image from the first image collection and one image from the second image collection. 10. The method of claim 8, wherein step cii) includes computing a distance or estimated travel time between the image capture locations of at least one image from the first image collection and one image from the second image collection. 11. The method of claim 3, wherein step cii) includes using a label from at least one of the image collections to produce the similarity score. 12. The method of claim 11, wherein the label is a name associated with a person. 13. The method of claim 3, wherein step cii) includes producing features associated with the distinct objects and determining the likelihood that a unique object from the first image collection also appears in the second image collection based on the produced features. 14. The method of claim 13, wherein step cii) includes using a label associated with a unique object from at least one of the image collections. 15. The method of claim 14, wherein the label is a name associated with a person and the unique object is a human face. 16. The method of claim 15, wherein the produced features are related to facial geometry. 17. The method of claim 13, wherein step cii) includes using a least one label associated with a unique object from the first image collection and one label associated with a unique object from the second image collection and determining the likelihood that a unique object from the first image collection also appears in the second image collection based on the labels. 18. The method of claim 17, wherein the labels are names associated with people and the unique object is a human face. 19. The method of claim 1, wherein step d) is also based on one or more sharing rules. 20. The method of claim 1, wherein step c) includes: i) detecting at least one unique object in the first image collection; ii) producing features associated with the distinct object; and iii) producing the similarity score based on the features and an analysis of the second image collection. 21. The method of claim 20, wherein a label is provided for the at least one unique object from the first image collection and the similarity score is produced based on the label, the features, and an analysis of the second image collection. 22. The method of claim 21, wherein the label is a name associated with a person and the unique object is a human face. 23. The method of claim 22, wherein the label is the first name of a person.