Collective Vision: Using Extremely Large Photograph Collections

2. Last Time Distributed Collaboration Google Goggles Personal object recognition World-Wide Landmark Recognition Building the engine

3. Today

4. Unsupervised learning of landmark images

5. Object matching based on local features

6. Match Region Graph

7. False detected images

8. Landmark Recognition All local features indexed in one k-d tree Match region - interest points that contribute to a match between two images

9. k-d trees k-dimensional binary tree Sub-trees split at median w.r.t one dim Cycle through dimensions Creates �bins� of NNs

10. Landmark Recognition Detect features on query image For each feature in query image Find NN features using k-d tree NN features link to their model image Score match regions between query and model images

11. Scoring Match Regions Query image interest points matching points in model image determined through NN search Match score = 1-PFPij (probability match b/w regions is false positive) PFPij is based on the number of matched points Match threshold = total score > 5

12. Intuition Query image should have many interest points with matches in match region = high match score Points should have matches in multiple regions (images) - threshold

13. Building Rome in a Day Use photos from photo-sharing websites to build 3D models of cities Web photos less structured than automated image capture (e.g. aerial) Increased efficiency through distributed computations

14. Multi-Stage Parallel Matching

15. Conclusion

16. Thoughts for Discussion Geo-clustering to filter out seldom traveled/photographed sites Match region graph for view comparison Pre-tag landmarks such as exits Augmented reality Distributed matching of features Ad-hoc wireless network range Other thoughts...