System to compile landmark image search results

1. A method to compile a landmark image search result, the method comprising:

receiving, using a computer, a plurality of images and data for each of the images, the data comprising a location corresponding to each image;

grouping the images into a plurality of location clusters based on the location corresponding to each image, each location cluster comprising at least one image;

grouping images of each location cluster into a plurality of visual clusters based on visual features of each image;

determining a rank of each image within each visual cluster according to at least one of a low-level self-similarity score, a low-level discriminative modeling score, and a point wise linking score; and

compiling the landmark image search result as a function of the rank of each image.

2. The method of claim 1 , further comprising:

determining a rank of each visual cluster of a set of visual cluster utilizing at least one of a number of users score, a visual coherence score, a cluster connectivity score, and a variability in dates score,

where determining a rank of each image includes determining a rank of each image within a visual cluster for each visual cluster, and

where compiling the landmark image search result includes compiling the landmark image search result as a function of both the rank of each visual cluster and the rank of each image.

3. The method of claim 2 , where the visual coherence score is based on the ratio of an inter-cluster distance to an intra-cluster distance.

4. The method of claim 2 , where the cluster connectivity score is obtained in part by determining a forward Euclidean distance between a descriptor in a first image and a descriptor in a second image and determining a reverse Euclidean distance between a descriptor in the second image and a descriptor in the first image.

5. The method of claim 2 , where the cluster connectivity score is obtained in part by determining whether a forward Euclidean distance plus a predetermined threshold is less than a forward distance between a descriptor in a first image and all other points in a second image.

6. The method of claim 2 , where the cluster connectivity score is obtained in part by determining an average number of links per photo for all the photos in a visual cluster.

7. The method of claim 1 , where the low-level self-similarity score is obtained in part by determining the Euclidean distance that an image resides from a centroid, where the centroid is based on local geometric descriptors.

8. The method of claim 1 , where the low-level discriminative modeling score is obtained in part by assigning a set of candidate photos as pseudo-positives and assigning randomly sampled images as pseudo-negatives.

9. The method of claim 1 , where the low-level discriminative modeling score is obtained in part by producing a support vector machine decision boundary, where the support vector machine decision boundary is produced by applying a first model from a first support vector machine to the contents of a second fold and applying a second model from a second support vector machine to the contents of a first fold.

10. A system, comprising at least one processor and memory., to compile landmark image search results, the system comprising:

a module for receiving a plurality of images and data for each of the images, the data comprising a location corresponding to each image;

a module for grouping the images into a plurality of location clusters based on the location corresponding to each image, each location cluster comprising at least one image;

a module for grouping images of each location cluster into a plurality of visual clusters based on visual features of each image;

a module for ranking images in each visual cluster to determine a rank of each image within the visual cluster according to at least one of a low-level self-similarity score, a low-level discriminative modeling score, and a point wise linking score; and

a ranked list generator module to compile the landmark image search result as a function of the rank of each image.

11. The system of claim 10 , further comprising:

a module for ranking visual clusters to determine a rank of each visual cluster utilizing at least one of a number of users score, a visual coherence score, a cluster connectivity score, and a variability in dates score,

where the ranking images in a visual cluster includes determining a rank of each image within a visual cluster for each visual cluster, and

where the ranked list generator to compile the landmark image search result further is to compile the landmark image search result as a function of both the rank of each visual cluster and the rank of each image.

12. The system of claim 11 , where the visual coherence score is based on the ratio of an inter-cluster distance to an intra-cluster distance.

13. The system of claim 11 , where the cluster connectivity score is obtained in part by determining a forward Euclidean distance between a descriptor in a first image and a descriptor in a second image and determining a reverse Euclidean distance between a descriptor in the second image and a descriptor in the first image.

14. The system of claim 11 , where the cluster connectivity score is obtained in part by determining whether a forward Euclidean distance plus a predetermined threshold is less than a forward distance between a descriptor in a first image and all other points in a second image.

15. The system of claim 11 , where the cluster connectivity score is obtained in part by determining an average number of links per photo for all the photos in a visual cluster.

16. The system of claim 10 , where the low-level self-similarity score is obtained in part by determining the Euclidean distance that an image resides from a centroid, where the centroid is based on local geometric descriptors.

17. The system of claim 10 , where the low-level discriminative modeling score is obtained in part by assigning a set of candidate photos as pseudo-positives and assigning randomly sampled images as pseudo-negatives.

18. The system of claim 10 , where the low-level discriminative modeling score is obtained in part by producing a support vector machine decision boundary, where the support vector machine decision boundary is produced by applying a first model from a first support vector machine to the contents of a second fold and applying a second model from a second support vector machine to the contents of a first fold.

19. A non-transitory computer readable medium comprising a set of instructions which, when executed by a computer, cause the computer to compile landmark image search results, the instructions for:

receiving a plurality of images and data for each of the images, the data comprising a location corresponding to each image;

grouping the images into a plurality of location clusters based on the location corresponding to each image, each location cluster comprising at least one image;

grouping images of each location cluster into a plurality of visual clusters based on visual features of each image;

determining a rank of each image within each visual cluster according to at least one of a low-level self-similarity score, a low-level discriminative modeling score, and a point wise linking score; and

compiling the landmark image search result as a function of the rank of each image.

20. The computer readable medium of claim 19 , further comprising instructions for:

determining a rank of each visual cluster of a set of visual cluster utilizing at least one of a number of users score, a visual coherence score, a cluster connectivity score, and a variability in dates score,

where determining a rank of each image includes determining a rank of each image within a visual cluster for each visual cluster, and

where compiling the landmark image search result includes compiling the landmark image search result as a function of both the rank of each visual cluster and the rank of each image.

21. The computer readable medium of claim 20 , where the cluster connectivity score is obtained in part by determining a forward Euclidean distance between a descriptor in a first image and a descriptor in a second image and determining a reverse Euclidean distance between a descriptor in the second image and a descriptor in the first image.

22. The computer readable medium of claim 20 , where the cluster connectivity score is obtained in part by determining whether a forward Euclidean distance plus a predetermined threshold is less than a forward distance between a descriptor in a first image and all other points in a second image.

23. The computer readable medium of claim 19 , where the low-level discriminative modeling score is obtained in part by assigning a set of candidate photos as pseudo-positives and assigning randomly sampled images as pseudo-negatives.

24. The computer readable medium of claim 19 , where the low-level discriminative modeling score is obtained in part by producing a support vector machine decision boundary, where the support vector machine decision boundary is produced by applying a first model from a first support vector machine to the contents of a second fold and applying a second model from a second support vector machine to the contents of a first fold.