Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
IEEE/ACM Transactions on Networking (TON)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Object Mining Using a Matching Graph on Very Large Image Collections
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Large-Scale Discovery of Spatially Related Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Location recognition using prioritized feature matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image retrieval with geometry-preserving visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Contextual weighting for vocabulary tree based image retrieval
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
Exhaustive pairwise matching on large datasets presents serious practical challenges, and has mostly remained an unexplored domain. We make a step in this direction by demonstrating the feasibility of scalable indexing and fast retrieval of appearance and geometric information in images. We identify unification of database filtering and geometric verification steps as a key step for doing this. We devise a novel inverted indexing scheme, based on Bloom filters, to scalably index high order features extracted from pairs of nearby features. Unlike a conventional inverted index, we can adapt the size of the inverted index to maintain adequate sparsity of the posting lists. This ensures constant time query retrievals. We are thus able to implement an exhaustive pairwise matching scheme, with linear time complexity, using the 'query each image in turn' technique. We find the exhaustive nature of our approach to be very useful in mining small clusters of images, as demonstrated by a 73.2% recall on the UKBench dataset. In the Oxford Buildings dataset, we are able to discover all the query buildings. We also discover interesting overlapping images connecting distant images.