A Simple Algorithm for Nearest Neighbor Search in High Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
TOCSAC: TOpology Constraint SAmple Consensus for Fast and Reliable Feature Correspondence
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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Multi-view correspondence of wide-baseline image matching is still a challenge task in computer vision. There are two main steps in dealing with correspondence issue: feature description and similarity search. The well-known SIFT descriptor is shown to be a-state-of-art descriptor which could keep distinctive invariant under transformation, large scale changes, noises and even small view point changes. This paper uses the SIFT as feature descriptor, and proposes a new search algorithm for similarity search. The proposed dichotomy based hash (DBH) method performs better than the widely used BBF (Best Bin First) algorithm, and also better than LSH (Local Sensitive Hash). DBH algorithm can obtain much higher (1-precision)-recall ratio in different kinds of image pairs with rotation, scale, noises and weak affine changes. Experimental results show that DBH can obviously improve the search accuracy in a shorter time, and achieve a better coarse match result.