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High-dimensional visual similarity search: k-d Generalized Randomized Forests

Content Provider	ACM Digital Library
Author	Avrithis, Yannis Samaras, Georgios Emiris, Ioannis Z.
Abstract	We propose a new data-structure, the generalized randomized k-d forest, or k-d GeRaF, for approximate nearest neighbor searching in high dimensions. In particular, we introduce new randomization techniques to specify a set of independently constructed trees where search is performed simultaneously, hence increasing accuracy. We omit backtracking, and we optimize distance computations. We release public domain software GeRaF and we compare it to existing implementations of state-of-the-art methods. Experimental results on SIFT and GIST visual descriptors, indicate that our method is the method of choice in dimensions around 1,000, and probably up to 10,000, and datasets of cardinality up to a few hundred thousands or even one million. For instance, we handle a real dataset of 106 GIST images represented in 960 dimensions with a query time of less than 1 sec on average and 90 responses being true nearest neighbors.
Starting Page	25
Ending Page	28
Page Count	4
File Format	PDF
ISBN	9781450341233
DOI	10.1145/2949035.2949042
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Gist images Image search High dimension Space partition Open software Randomized tree
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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High-dimensional approximate nearest neighbor : k-d Generalized Randomized Forests

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