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» Structured metric learning for high dimensional problems
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STOC
1998
ACM
190views Algorithms» more  STOC 1998»
13 years 11 months ago
Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vecto...
Eyal Kushilevitz, Rafail Ostrovsky, Yuval Rabani
VLDB
1999
ACM
118views Database» more  VLDB 1999»
13 years 11 months ago
Similarity Search in High Dimensions via Hashing
The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing ...
Aristides Gionis, Piotr Indyk, Rajeev Motwani
MICCAI
2000
Springer
13 years 11 months ago
Small Sample Size Learning for Shape Analysis of Anatomical Structures
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
CVPR
2008
IEEE
14 years 9 months ago
Fast image search for learned metrics
We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis d...
Prateek Jain, Brian Kulis, Kristen Grauman
SIGMOD
2002
ACM
219views Database» more  SIGMOD 2002»
14 years 7 months ago
Efficient k-NN search on vertically decomposed data
Applications like multimedia retrieval require efficient support for similarity search on large data collections. Yet, nearest neighbor search is a difficult problem in high dimen...
Arjen P. de Vries, Nikos Mamoulis, Niels Nes, Mart...