Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Fast Pose Estimation with Parameter-Sensitive Hashing
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
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Binary SIFT: towards efficient feature matching verification for image search
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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We propose a spatial min-Hash algorithm that groups the minimal hashing functions into an s-tuples called a sketch depending on the spatial context. We use the bag-of-words technology to represent an image in a spatial pyramid way, and generate a minimal hashing function for each spatial location of the corresponding level. These minimal hashing functions are bundled to form a sketch. Furthermore, we implement the proposed algorithm to similar image searching. We use the binary SIFT combined with Hamming distance to verify the candidate images obtained by the spatial min-Hash in order to improve the retrieval performance. There are two advantages of our method: 1) the spatial min-Hash is more discriminative than the standard min-Hash in term of image representation; 2) the feature matching based on the binary SIFT in the verification stage improves the performance of image retrieval with a low computational cost. We implement our method on Oxford building dataset, and the experimental results demonstrate that the spatial min-Hash is a stronger representation method than the standard min-Hash, and the spatial min-Hash is superior to the standard min-Hash in term of retrieval performance.