Proceedings ArticleDOI
Data-oriented locality sensitive hashing
Wei Zhang,Ke Gao,Yongdong Zhang,Jintao Li +3 more
- pp 1131-1134
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TLDR
Data-Oriented LSH is proposed to reduce memory consumption when data are non-uniformly distributed and focused on the hash table construction, and thus the query-directed methods can be applied to the index to improve further.Abstract:
Locality Sensitive Hashing (LSH) has been proposed as a scalable and high-dimensional index for approximate similarity search. Euclidean LSH is a variation of LSH and has been successfully used in many multimedia applications. However, hash functions of the basic Euclidean LSH project data points over randomly selected directions, which reduces accuracy when data are non-uniformly distributed. So more hash tables are needed to guarantee the accuracy, and thus more memory is consumed. Since heavy memory cost is a significant drawback of Euclidean LSH, we propose Data-Oriented LSH to reduce memory consumption when data are non-uniformly distributed. Most of existing methods are query-directed, such as multi-probe and query expansion methods. We focused on the hash table construction, and thus the query-directed methods can be applied to our index to improve further. The experiment shows that to achieve the same accuracy, our method uses less time and less memory compared with original Euclidean LSH.read more
Citations
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Content based image retrieval using deep learning process
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Query by example in large-scale code repositories
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Compact Image Fingerprint Via Multiple Kernel Hashing
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Nonnegative sparse coding induced hashing for image copy detection
TL;DR: This work combines the constrained nonnegative sparse coding and the Support Vector Machine to propose a new hashing method, called non negative sparse coding induced hashing (NSCIH), which is superior to the state-of-the-art hashing methods.
References
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Proceedings ArticleDOI
Scalable Recognition with a Vocabulary Tree
David Nister,Henrik Stewenius +1 more
TL;DR: A recognition scheme that scales efficiently to a large number of objects and allows a larger and more discriminatory vocabulary to be used efficiently is presented, which it is shown experimentally leads to a dramatic improvement in retrieval quality.
Journal ArticleDOI
A Comparison of Affine Region Detectors
Krystian Mikolajczyk,Tinne Tuytelaars,Cordelia Schmid,Andrew Zisserman,Jiri Matas,Frederik Schaffalitzky,Timor Kadir,L. Van Gool +7 more
TL;DR: A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework.
Proceedings ArticleDOI
Locality-sensitive hashing scheme based on p-stable distributions
TL;DR: A novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions that improves the running time of the earlier algorithm and yields the first known provably efficient approximate NN algorithm for the case p<1.
Journal ArticleDOI
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Alexandr Andoni,Piotr Indyk +1 more
TL;DR: An algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O(dn 1c2/+o(1)) and space O(DN + n1+1c2 + o(1) + 1/c2), which almost matches the lower bound for hashing-based algorithm recently obtained.
Book
Similarity Search: The Metric Space Approach
TL;DR: Similarity Search focuses on the state of the art in developing index structures for searching the metric space, and provides an extensive survey of specific techniques for a large range of applications.