Landmarks from digital photo collections

1. A method for detecting and annotating landmarks in digital images:

(a) automatically assigning, to one or more images in a plurality of text-associated digital images, a text tag descriptive of a landmark, to generate a set of landmark-tagged images, wherein images in the set of landmark-tagged images are algorithmically determined to include the landmark, wherein the text tag is based upon one or more scored n-grams from one or more texts associated with the one or more images, wherein the n-grams are scored by assigning correlation-weights to the plurality of text-associated digital images, and wherein the landmark identifies an object in or feature marking a geographic point or geographic area;

(b) learning an appearance model for the landmark from the set of landmark-tagged images; and

(c) detecting the landmark in a new image using the appearance model,

wherein said stages (a)-(c) are performed by at least one processor.

2. The method of claim 1 , further comprising:

(d) annotating the new image with the tag descriptive of the landmark.

3. The method of claim 1 , wherein stage (a) comprises:

(i) generating a list of landmark n-grams from the plurality of text-associated digital images;

(ii) computing an n-gram score for each landmark n-gram in an n-gram set, wherein the n-gram set is a subset of the list of landmark n-grams; and

(iii) assigning a tag descriptive of a landmark, wherein the tag descriptive of the landmark is based on at least one landmark n-gram in said n-gram set.

4. The method of claim 3 , wherein stage (a)(i) comprises:

electronically accessing the plurality of text-associated digital images; and

retrieving at least one of said landmark n-grams from a text associated with an image in the plurality of text-associated digital images.

5. The method of claim 3 , wherein stage (a)(ii) comprises:

assigning said correlation weights to the plurality of text-associated digital images, wherein each image in the plurality of text-associated digital images is assigned a correlation weight based on correlation of metadata of said each image with other images in the plurality of text-associated digital images, and wherein the plurality of text-associated digital images includes the set of text-associated images;

generating a matching-images graph from the plurality of text-associated digital images, wherein each edge in the matching-images graph is assigned a match score representing a level of match between two images connected by said each edge; and

linking said landmark n-grams to images in the plurality of text-associated digital images based upon said correlation-weights and the matching images graph to generate links between landmark n-grams and images in the plurality of text-associated digital images.

6. The method of claim 5 , wherein stage (a)(ii) further comprises:

estimating a geo-reliability score for each image of the plurality of text-associated digital images using the matching-images graph, wherein the geo-reliability score is an estimation of accuracy of geo-location information of said each image based on a comparison of visual consistency of other images with geo-location coordinates within a predetermined distance to said each image.

7. The method of claim 6 , wherein stage (a)(ii) further comprises:

computing a variance of geo-location for a landmark n-gram of said n-gram set, wherein the variance is based on geo-locations of images linked to said landmark n-gram in said matching-images graph; and

removing from said n-gram set any landmark n-grams having a variance of geo-location exceeding a predetermined threshold.

8. The method of claim 5 , wherein the n-gram score is based on the matching-images graph.

9. The method of claim 8 , wherein the n-gram score is computed as a ratio of strength of internal edges of said matching-images graph and the strength of external edges of said matching images graph, wherein an internal edge exists between images having at least one common landmark n-gram, and wherein an external edge exists between images not having at least one common landmark n-gram.

10. The method of claim 5 , wherein stage (a)(i) further comprises:

merging two or more landmark n-grams in said n-gram set based at least on one of,

a similarity of the score of said two or more landmark n-grams, and

an overlap of images having said two or more landmark n-grams in linked landmark n-grams.

11. The method of claim 5 , wherein the metadata includes information relating to at least one of,

an author,

a geo-location, and

a time of origin.

12. The method of claim 5 , wherein each link in the matching-graph represents matching feature descriptors between two images of the plurality of text-associated digital images.

13. A system for automatically detecting and annotating landmarks in digital images, comprising:

at least one collection of text-associated digital images stored in a memory medium; and

at least one processor communicatively coupled to said medium, the at least one processor configured to:

automatically assign, to one or more images in a plurality of text-associated digital images, a text tag descriptive of a landmark, to generate a set of landmark-tagged images, wherein images in the set of landmark-tagged images are algorithmically determined to include the landmark, wherein the text tag is based upon one or more scored n-grams from one or more texts associated with the one or more images, wherein the n-grams are scored by assigning correlation-weights to the plurality of text-associated digital images, and wherein the landmark identifies an object in or feature marking a geographic point or geographic area;

learn an appearance model for the landmark from the set of landmark-tagged images; and

detect the landmark in a new image using the appearance model.

14. The system of claim 13 , wherein the at least one processor is further configured to:

annotate the new image with the tag descriptive of the landmark.

15. The system of claim 13 , wherein the at least one processor is further configured to:

generate a list of landmark n-grams from the plurality of text-associated digital images;

compute an n-gram score for each landmark n-gram in an n-gram set, wherein the n-gram set is a subset of the list of landmark n-grams; and

assign a tag descriptive of a landmark, wherein the tag descriptive of the landmark is based on at least one landmark n-gram in said n-gram set.

16. The system of claim 15 , wherein the at least one processor is further configured to:

assigning said correlation weights to the plurality of text-associated digital images, wherein each image in the plurality of text-associated digital images is assigned a correlation weight based on correlation of metadata of said each image with other images in the plurality of text-associated digital images, and wherein the plurality of text-associated digital images includes the set of text-associated images;

generate a matching-images graph from the plurality of text-associated digital images, wherein each edge in the matching-images graph is assigned a match score representing a level of match between two images connected by said each edge; and

link said landmark n-grams to images in the plurality of text-associated digital images based upon said correlation-weights and the matching images graph to generate links between landmark n-grams and images in the plurality of text-associated digital images.

17. A non-transitory computer readable storage medium having computer program logic recorded thereon for enabling a processor to name images, said computer program logic comprising:

a first module configured to enable the processor to assign, to one or more images in a plurality of text-associated digital images, a text tag descriptive of a landmark, to generate a set of landmark-tagged images, wherein images in the set of landmark-tagged images are algorithmically determined to include the landmark, wherein the text tag is based upon one or more scored n-grams from one or more texts associated with the one or more images, wherein the n-grams are scored by assigning correlation-weights to the plurality of text-associated digital images, and wherein the landmark identifies an object in or feature marking a geographic point or geographic area;

a second module configured to enable the processor to learn an appearance model for the landmark from the set of landmark-tagged images;

a third module configured to enable the processor to detect the landmark in a new image using the appearance model.

18. The non-transitory computer readable storage medium of claim 17 , further comprising:

a fourth module configured to enable the processor to annotate the new image with the tag descriptive of the landmark.

19. The non-transitory computer readable storage medium of claim 17 , wherein the first module is further configured to:

generate a list of landmark n-grams from the plurality of text-associated digital images;

compute an n-gram score for each landmark n-gram in an n-gram set, wherein the n-gram set is a subset of the list of landmark n-grams; and

assign a tag descriptive of a landmark, wherein the tag descriptive of the landmark is based on at least one landmark n-gram in said n-gram set.

20. The non-transitory computer readable storage medium of claim 19 , wherein the first module is further configured to:

assigning said correlation weights to the plurality of text-associated digital images, wherein each image in the plurality of text-associated digital images is assigned a correlation-weight based on correlation of metadata of said each image with other images in the plurality of text-associated digital images, and wherein the plurality of text-associated digital images includes the set of text-associated images;

generate a matching-images graph from the plurality of text-associated digital images, wherein each edge in the matching images graph is assigned a match score representing a level of match between two images connected by said each edge; and

link said landmark n-grams to images in the plurality of text-associated digital images based upon said correlation-weights and the matching images graph to generate links between landmark n-grams and images in the plurality of text-associated digital images.