A Simple Algorithm for Nearest Neighbor Search in High Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Robust multi-view feature matching from multiple unordered views
Pattern Recognition
Vocabulary-based hashing for image search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Distance-Based multiple paths quantization of vocabulary tree for object and scene retrieval
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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High-dimensional image feature matching is an important part of many image matching based problems in computer vision which are solved by local invariant features. In this paper, we propose a new indexing/searching method based on Randomized Sub-Vectors Hashing (called RSVH) for high-dimensional image feature matching. The essential of the proposed idea is that the feature vectors are considered similar (measured by Euclidean distance) when the L2 norms of their corresponding randomized sub-vectors are approximately same respectively. Experimental results have demonstrated that our algorithm can perform much better than the famous BBF (Best-Bin-First) and LSH (Locality Sensitive Hashing) algorithms in extensive image matching and image retrieval applications.