Article

Sequential projection learning for hashing with compact codes

Authors:

Jun Wang,

Sanjiv Kumar,

Shih-Fu ChangAuthors Info & Claims

ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning

Pages 1127 - 1134

Published: 21 June 2010 Publication History

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Abstract

Hashing based Approximate Nearest Neighbor (ANN) search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing methods either use random projections or extract principal directions from the data to derive hash functions. The resulting embedding suffers from poor discrimination when compact codes are used. In this paper, we propose a novel data-dependent projection learning method such that each hash function is designed to correct the errors made by the previous one sequentially. The proposed method easily adapts to both unsupervised and semi-supervised scenarios and shows significant performance gains over the state-of-the-art methods on two large datasets containing up to 1 million points.

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Cited By

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Jhuo I(2019)Supervised set-to-set hashing in visual recognitionProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367147(803-810)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367147
Yang YGeng LLai HPan YYin JEl Saddik ADel Bimbo AZhang ZHauptmann ACandan KBertini MXie LWei X(2019)Feature Pyramid HashingProceedings of the 2019 on International Conference on Multimedia Retrieval10.1145/3323873.3325015(114-122)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3323873.3325015
Zhang JPeng Y(2019)SSDHIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.277133229:1(212-225)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TCSVT.2017.2771332
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Sequential projection learning for hashing with compact codes
1. Computing methodologies
  1. Machine learning

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Published In

ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning

June 2010

1262 pages

ISBN:9781605589077

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Omnipress

Madison, WI, United States

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Published: 21 June 2010

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Jhuo I(2019)Supervised set-to-set hashing in visual recognitionProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367147(803-810)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367147
Yang YGeng LLai HPan YYin JEl Saddik ADel Bimbo AZhang ZHauptmann ACandan KBertini MXie LWei X(2019)Feature Pyramid HashingProceedings of the 2019 on International Conference on Multimedia Retrieval10.1145/3323873.3325015(114-122)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3323873.3325015
Zhang JPeng Y(2019)SSDHIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.277133229:1(212-225)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TCSVT.2017.2771332
Ma YYang DXie HYin J(2019)Supervised deep hashing for image content securityMultimedia Tools and Applications10.1007/s11042-017-5433-z78:1(661-676)Online publication date: 1-Jan-2019
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Sharma SGupta VJuneja M(2019)A survey of image data indexing techniquesArtificial Intelligence Review10.1007/s10462-018-9673-852:2(1189-1266)Online publication date: 1-Aug-2019
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Wang KGeng X(2018)Binary coding based label distribution learningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3305047(2783-2789)Online publication date: 13-Jul-2018
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Shu XJiang HXu H(2018)Graph regularized supervised cross-view hashingMultimedia Tools and Applications10.5555/3287850.328787877:21(28207-28224)Online publication date: 1-Nov-2018
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Cited By

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Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning

Complementary Projection Hashing

Supervised Discriminative Hashing for Compact Binary Codes

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