Size Matters: Exhaustive Geometric Verification for Image Retrieval Accepted for ECCV 2012
- Henrik StewéniusAffiliated withGoogle
- , Steinar H. GundersonAffiliated withGoogle
- , Julien PiletAffiliated withGoogle
The overreaching goals in large-scale image retrieval are bigger, better and cheaper. For systems based on local features we show how to get both efficient geometric verification of every match and unprecedented speed for the low sparsity situation.
Large-scale systems based on quantized local features usually process the index one term at a time, forcing two separate scoring steps: First, a scoring step to find candidates with enough matches, and then a geometric verification step where a subset of the candidates are checked.
Our method searches through the index a document at a time, verifying the geometry of every candidate in a single pass. We study the behavior of several algorithms with respect to index density—a key element for large-scale databases. In order to further improve the efficiency we also introduce a new new data structure, called the counting min-tree, which outperforms other approaches when working with low database density, a necessary condition for very large-scale systems.
We demonstrate the effectiveness of our approach with a proof of concept system that can match an image against a database of more than 90 billion images in just a few seconds.
- Size Matters: Exhaustive Geometric Verification for Image Retrieval Accepted for ECCV 2012
- Book Title
- Computer Vision – ECCV 2012
- Book Subtitle
- 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II
- pp 674-687
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Industry Sectors
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- Editor Affiliations
- 16. Microsoft Research Ltd
- 17. Dept. of Computer Science, University of North Carolina
- 18. California Institute of Technology
- 19. Institute of Industrial Science, The University of Tokyo
- 20. INRIA
- Author Affiliations
- 21. Google, Switzerland
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