Article

BoostMap: a method for efficient approximate similarity rankings

Authors:

Vassilis Athitsos,

George KolliosAuthors Info & Claims

CVPR'04: Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Pages 268 - 275

Published: 27 June 2004 Publication History

Abstract

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, 1D embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures. In both systems, in quantitative experiments, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.

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cover image Guide Proceedings

CVPR'04: Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

June 2004

1041 pages

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IEEE-CS\DATC: IEEE Computer Society

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IEEE Computer Society

United States

Publication History

Published: 27 June 2004

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Cited By

Gao JJagadish HOoi BWang SCao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Selective HashingProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783284(349-358)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2783284
Tencer LReznakova MCheriet M(2015)TITS-FMApplied Soft Computing10.1016/j.asoc.2014.09.02426:C(531-544)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.asoc.2014.09.024
Gao JJagadish HLu WOoi BDyreson CLi FÖzsu M(2014)DSHProceedings of the 2014 ACM SIGMOD International Conference on Management of Data10.1145/2588555.2588565(1127-1138)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2588555.2588565
Boytsov LNaidan B(2013)Learning to prune in metric and non-metric spacesProceedings of the 27th International Conference on Neural Information Processing Systems - Volume 110.5555/2999611.2999787(1574-1582)Online publication date: 5-Dec-2013
https://dl.acm.org/doi/10.5555/2999611.2999787
Papapetrou PChistiakova THollmén JKalogeraki VGunopulos DMakedon F(2012)Finding representative objects using link analysis rankingProceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2413097.2413105(1-4)Online publication date: 6-Jun-2012
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Mohr DZachmann G(2011)Segmentation-free, area-based articulated object trackingProceedings of the 7th international conference on Advances in visual computing - Volume Part I10.5555/2045110.2045124(112-123)Online publication date: 26-Sep-2011
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Papapetrou PAthitsos VPotamias MKollios GGunopulos D(2011)Embedding-based subsequence matching in time-series databasesACM Transactions on Database Systems10.1145/2000824.200082736:3(1-39)Online publication date: 26-Aug-2011
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Van Leuken RVeltkamp R(2011)Selecting vantage objects for similarity indexingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/2000486.20004907:3(1-18)Online publication date: 2-Sep-2011
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Skopal TBustos B(2011)On nonmetric similarity search problems in complex domainsACM Computing Surveys10.1145/1978802.197881343:4(1-50)Online publication date: 18-Oct-2011
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