Query evaluation: strategies and optimizations
Information Processing and Management: an International Journal
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Efficient passage ranking for document databases
ACM Transactions on Information Systems (TOIS)
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Accurate Image Search Using the Contextual Dissimilarity Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local bag-of-features model for large-scale object retrieval
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Descriptor learning for efficient retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Towards large-scale geometry indexing by feature selection
Computer Vision and Image Understanding
Hi-index | 0.00 |
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.