Efficient Anchor Graph Hashing with Data-Dependent Anchor Selection

Hiroaki TAKEBE; Yusuke UEHARA; Seiichi UCHIDA

doi:10.1587/transinf.2015EDL8060

Efficient Anchor Graph Hashing with Data-Dependent Anchor Selection

Hiroaki TAKEBE, Yusuke UEHARA, Seiichi UCHIDA

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Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space.

Publication: IEICE TRANSACTIONS on Information Vol.E98-D No.11 pp.2030-2033

Publication Date: 2015/11/01

Publicized: 2015/08/17

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDL8060

Type of Manuscript: LETTER

Category: Image Recognition, Computer Vision

Authors

Hiroaki TAKEBE
  FUJITSU LABORATORIES LTD.
Yusuke UEHARA
  FUJITSU LABORATORIES LTD.
Seiichi UCHIDA
  Kyushu University

Keyword

nearest neighbor search, anchor graph hashing, similarity space, principal component analysis

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Hiroaki TAKEBE, Yusuke UEHARA, Seiichi UCHIDA, "Efficient Anchor Graph Hashing with Data-Dependent Anchor Selection" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 11, pp. 2030-2033, November 2015, doi: 10.1587/transinf.2015EDL8060.
Abstract: Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8060/_p

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@ARTICLE{e98-d_11_2030,
author={Hiroaki TAKEBE, Yusuke UEHARA, Seiichi UCHIDA, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Anchor Graph Hashing with Data-Dependent Anchor Selection},
year={2015},
volume={E98-D},
number={11},
pages={2030-2033},
abstract={Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space.},
keywords={},
doi={10.1587/transinf.2015EDL8060},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Efficient Anchor Graph Hashing with Data-Dependent Anchor Selection
T2 - IEICE TRANSACTIONS on Information
SP - 2030
EP - 2033
AU - Hiroaki TAKEBE
AU - Yusuke UEHARA
AU - Seiichi UCHIDA
PY - 2015
DO - 10.1587/transinf.2015EDL8060
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2015
AB - Anchor graph hashing (AGH) is a promising hashing method for nearest neighbor (NN) search. AGH realizes efficient search by generating and utilizing a small number of points that are called anchors. In this paper, we propose a method for improving AGH, which considers data distribution in a similarity space and selects suitable anchors by performing principal component analysis (PCA) in the similarity space.
ER -