Joint Inverted Indexing

Inverted indexing is a popular non-exhaustive solution to large scale search. An inverted file is built by a quantizer such as k-means or a tree structure. It has been found that multiple inverted files, obtained by multiple independent random quantizers, are able to achieve practically good recall...

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Bibliographic Details
Published in2013 IEEE International Conference on Computer Vision pp. 3416 - 3423
Main Authors Xia, Yan, He, Kaiming, Wen, Fang, Sun, Jian
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Computer vision
Conferences
Counters
Indexing
Joints
Lattices
Mathematical analysis
Optimization
Quantization (signal)
Recall
Search engines
Searching
State of the art
Vectors
Vectors (mathematics)
Online AccessGet full text
ISSN1550-5499
DOI10.1109/ICCV.2013.424

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Summary:Inverted indexing is a popular non-exhaustive solution to large scale search. An inverted file is built by a quantizer such as k-means or a tree structure. It has been found that multiple inverted files, obtained by multiple independent random quantizers, are able to achieve practically good recall and speed. Instead of computing the multiple quantizers independently, we present a method that creates them jointly. Our method jointly optimizes all code words in all quantizers. Then it assigns these code words to the quantizers. In experiments this method shows significant improvement over various existing methods that use multiple independent quantizers. On the one-billion set of SIFT vectors, our method is faster and more accurate than a recent state-of-the-art inverted indexing method.
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SourceType-Conference Papers & Proceedings-2
ISSN:1550-5499
DOI:10.1109/ICCV.2013.424