Peter's SVR Notebook
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ICCV 2009 Paper: Left to do
- Verify that re-ranking works for some datapoints.
- Look at how LSH speed varies with database size.
- How do the no. of model-matches (to correct model) drop off with database size?
- 500K vocabulary for houses.
- Fair plot for Philbin.
- Why does Philbin break down.
- Understand why optimization for LSH didn't work, and why it works now. Need for discussion.
Core Message
- Which method is good to use of SIFT?
- Which one scales the best?
- Which one is fastest?
- Which one is most accurate?
Speed Issues
- For Philbin's approach, is the computation really logarithmic? How many matches do we get for different database sizes?
Memory Issues
LSH
- What are the inherent problems in the LSH algorithm for SIFT descriptors?
- Cannot have R too small, as we will miss good NNs.
- Cannot have R too large, as we will get too many bad NNs.
- In practice, this will lead to linear scaling in computation time for large databases.
- Try using fixed L-prime and see how accuracy degrades (more realistic setting).
- Sanity-check matches to see that inliers are really inliers.
- Why didn't original tuning work?
- It is important to pick a dataset with realistic query points -- i.e. not spurious points far from everything, but points that acutally survived RANSAC.
- Show how 3 sets of points give vastly different parameter settings. (pts surviving ransac, pts NOT surviving ransac, and no such consideration at all).
- Also need low prob -- 90% is ridicolous.
- How about R.