Discovering City Landmarks from Online Journals

• US 20100205176A1
• Filed: 02/12/2009
• Published: 08/12/2010
• Est. Priority Date: 02/12/2009
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Ji, Rongrong, Ma, Wei-Ying, Xie, Xing
• Application Number:
  US12/370,270
• Publication Number:
  US 20100205176A1
• Time in Patent Office: Days
• US Class Current: 707/737
• 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: G06V 20/39 Urban scenes

Abstract

A blog-based city landmark discovery framework is described to discover and summarize popular scenes and their representative views from blog photos to provide online personalized tourist suggestions. First, a location extraction algorithm is implemented to infer geographical associations of blog photos from their contextual descriptors, thus providing the ability to harvest city scene photos from web blogs. Second, a visual-textual hierarchical clustering scheme is adopted to organize crawled photos into a scene-view structure, and present a PhotoRank algorithm to discover representative views within each scene by viewing the representative photo selection problem as a popularity ranking problem in a visual correlation environment. Third, author, context and content issues are evaluated in a unified Landmark-HITS model to discover representative scenes as well as build author correlations. The author correlations further facilitate a collaborative filtering process for online personalized tourist suggestions based on an author'"'"'s previous travel logs.

Claims

1. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising:

identifying one or more photographs from a plurality of online journals;

clustering the one or more photographs into one or more views from which the one or more photographs have been captured;

modeling author, context and content information associated with the one or more views to discover one or more representative photographs and build author correlations;

filtering the author correlations and the one or more representative photographs; and

providing personalized recommendations to a user based at least in part on the filtering of the author correlations and the one or more representative photographs.

2. The one or more computer-readable media according to claim 1, wherein the one or more photographs contain one or more contextual descriptors, the one or more contextual descriptors used to create one or more geographical associations with the one or more photographs.

3. The one or more computer-readable media according to claim 2, wherein the context information includes the one or more geographical associations, title information, and user comments entered in the online journals.

4. The one or more computer-readable media according to claim 1, wherein identifying the one or more photographs from a plurality of online journals comprises analyzing a gazetteer to identify at least a portion of the one or more photographs.

5. The one or more computer-readable media according to claim 1, wherein the modeling includes an iterative discovery process used to discover the one or more representative photographs that are significant with respect to the author and context information.

6. The one or more computer-readable media according to claim 1, wherein the filtering is a collaborative filtering using preferences from a plurality of users and a target user.

7. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising:

identifying one or more photographs from a plurality of online journals;

storing the identified one or more photographs in a database;

clustering the one or more photographs into one or more views and into one or more textual descriptions;

modeling author, context and content information associated with the one or more views and the one or more textual descriptions to discover one or more representative photographs and create one or more author correlations; and

collaboratively filtering the one or more author correlations and the one or more representative photographs to provide a personalized recommendation to a user.

8. The one or more computer-readable media according to claim 7, wherein the correlations are filtered to determine relevant photographs from the one or more representative photographs provided for the personalized recommendation.

9. The one or more computer-readable media according to claim 8, wherein the filtered correlations use a collaborative filtering that combines preferences from a plurality of users and a target user.

10. The one or more computer-readable media according to claim 7, wherein the one or more photographs contain one or more contextual descriptors, the one or more contextual descriptors used to create one or more geographical associations with the one or more photographs.

11. The one or more computer-readable media according to claim 10, wherein the content information includes the one or more geographical associations, title information, and user comments entered in the online journals.

12. The one or more computer-readable media according to claim 7, wherein identifying the one or more photographs from a plurality of online journals comprises analyzing a gazetteer to identify at least a portion of the one or more photographs.

13. The one or more computer-readable media according to claim 7, wherein the modeling includes an iterative discovery process used to discover the one or more representative photographs that are within a scene by propagating photograph popularities based on the one or more author correlations.

14. The one or more computer-readable media according to claim 9, wherein the modeling includes an iterative discovery process used to discover the one or more representative photographs that are significant with respect to the author, context and content information.

15. A method for discovering one or more photographs from a plurality of online journals for providing a personalized recommendation comprising:

extracting the one or more photographs from the plurality of online journals;

storing the extracted one or more photographs in a database;

clustering the one or more photographs into one or more views and one or more textual descriptions;

modeling author, context and content information associated with the one or more views and the one or more textual descriptions to discover one or more representative photographs;

creating one or more correlations between an author, the one or more representative photographs and the one or more textual descriptions; and

providing a personal recommendation based at least in part on the created correlations.

16. The method according to claim 15, wherein. creating correlations further comprises conducting a filtering operation to define one or more relevant correlations.

17. The method according to claim 16, wherein the one or more relevant correlations are utilized at least in part to create the personal recommendation.

18. The method according to claim 15, wherein the one or more photographs contain one or more contextual descriptors, the one or more contextual descriptors used to create one or more geographical associations with the one or more photographs.

19. The method according to claim 18, wherein the content information includes the one or more geographical associations, title information, and user comments entered in the online journals.

20. The method according to claim 15, wherein identifying the one or more photographs from a plurality of online journals comprises analyzing a gazetteer to identify at least a portion of the one or more photographs.