Landmarks from digital photo collections

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

electronically accessing a plurality of digital images in an image collection;

retrieving an n-gram set generated from one or more texts associated with one or more images in the plurality of digital images;

choosing, from the n-gram set, one or more scored n-grams having at least a minimum reliability measure that is based on a number of unique authors of the one or more texts;

automatically assigning, to at least one of the one or more images in the image collection, a 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 tag is based upon the one or more chosen scored n-grams from the one or more texts associated with the at least one of the one or more images, wherein the landmark identifies a geographic point or geographic area, and prioritizing images of popular landmark locations within the image collection according to a landmark popularity measure for each landmark location represented within the image collection, wherein the landmark popularity measure is based on a number of images of respective landmark locations uploaded to a photo sharing website that stores the image collection;

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

detecting the landmark in a new image that is not part of the image collection using the appearance model, wherein the method is performed by at least one processor.

2. The method of claim 1 , further comprising annotating the new image with the tag descriptive of the landmark.

3. The method of claim 1 , further comprising:

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

generating a matching-images graph from the plurality of 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 each edge; and

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

4. The method of claim 3 , further comprising:

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

5. The method of claim 4 , further comprising:

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

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

6. The method of claim 3 , wherein an n-gram score of each scored n-gram in the n-gram set is based on the matching-images graph.

7. The method of claim 6 , wherein the n-gram score of each scored n-gram in the n-gram set is computed as a ratio of strength of internal edges of said matching-images graph and strength of external edges of said matching-images graph, wherein an internal edge exists between images having at least one common scored n-gram, and wherein an external edge exists between images not having at least one common scored n-gram.

8. The method of claim 3 , further comprising merging two or more scored n-grams in said n-gram set.

9. The method of claim 8 , wherein the merging is based at least on one of,

a similarity between respective scores of the two or more scored n-grams, and

an overlap of images having the two or more scored n-grams in linked scored n-grams.

10. The method of claim 3 , wherein the metadata includes information relating to at least one of:

an author,

a geo-location, or

a time of origin.

11. The method of claim 3 , wherein each link in the matching-images graph represents matching feature descriptors between two images of the plurality of digital images.

12. The method of claim 1 , wherein the appearance model includes visual information and non-visual information, and wherein the detecting the landmark includes using the visual information and the non-visual information to identify the landmark.

13. The method of claim 1 , wherein an n-gram score of each scored n-gram in the n-gram set is based upon a level of match between images associated with each scored n-gram and a level of correlation between each image associated with the landmark with other images associated with the landmark, and wherein each scored n-gram is associated with one or more images having associated text from which each scored n-gram was obtained.

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

automatically assigning, to one or more images in an image collection that includes a plurality of digital images, a 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 tag is based upon one or more scored n-grams from one or more texts associated with the one or more images, wherein the landmark identifies a geographic point or geographic area, and prioritizing images of popular landmark locations within the image collection according to a landmark popularity measure for each landmark location represented within the image collection, wherein the landmark popularity measure is based on a number of images of respective landmark locations uploaded to a photo sharing website that stores the image collection;

learning an appearance model for the landmark from the set of landmark-tagged images wherein the appearance model includes visual information and non-visual information; and

detecting the landmark in a new image that is not part of the image collection using the appearance model, wherein the detecting the landmark includes using the visual information and the non-visual information to identify the landmark, and wherein the method is performed by at least one processor.

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

automatically assigning, to one or more images in an image collection that includes a plurality of digital images, a 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 tag is based upon one or more scored n-grams from one or more texts associated with the one or more images, wherein an n-gram score of each of the one or more scored n-grams is based upon a level of match between images associated with each scored n-gram and a level of correlation between each image associated with the landmark with other images associated with the landmark, and wherein each scored n-gram is associated with one or more images having associated text from which each scored n-gram was obtained,

wherein the landmark identifies a geographic point or geographic area, and prioritizing images of popular landmark locations within the image collection according to a landmark popularity measure for each landmark location represented within the image collection, wherein the landmark popularity measure is based on a number of images of respective landmark locations uploaded to a photo sharing website that stores the image collection;

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

detecting the landmark in a new image that is not part of the image collection using the appearance model, wherein the method is performed by at least one processor.