The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Image similarity search with compact data structures
Proceedings of the thirteenth ACM international conference on Information and knowledge management
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Efficient filtering with sketches in the ferret toolkit
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Sizing sketches: a rank-based analysis for similarity search
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Efficient Similarity Search by Reducing I/O with Compressed Sketches
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
A sketch-based distance oracle for web-scale graphs
Proceedings of the third ACM international conference on Web search and data mining
Online generation of locality sensitive hash signatures
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Algorithm for detecting significant locations from raw GPS data
DS'10 Proceedings of the 13th international conference on Discovery science
Consistent visual words mining with adaptive sampling
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
TAKES: a fast method to select features in the kernel space
Proceedings of the 20th ACM international conference on Information and knowledge management
Asymmetric hamming embedding: taking the best of our bits for large scale image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Feature selection for link prediction
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Image search—from thousands to billions in 20 years
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
In-network approximate computation of outliers with quality guarantees
Information Systems
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Efficient similarity search in high-dimensional spaces is important to content-based retrieval systems. Recent studies have shown that sketches can effectively approximate L1 distance in high-dimensional spaces, and that filtering with sketches can speed up similarity search by an order of magnitude. It is a challenge to further reduce the size of sketches, which are already compact, without compromising accuracy of distance estimation. This paper presents an efficient sketch algorithm for similarity search with L2 distances and a novel asymmetric distance estimation technique. Our new asymmetric estimator takes advantage of the original feature vector of the query to boost the distance estimation accuracy. We also apply this asymmetric method to existing sketches for cosine similarity and L1 distance. Evaluations with datasets extracted from images and telephone records show that our L2 sketch outperforms existing methods, and the asymmetric estimators consistently improve the accuracy of different sketch methods. To achieve the same search quality, asymmetric estimators can reduce the sketch size by 10% to 40%.