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  1. 論文誌(トランザクション)
  2. Computer Vision and Applications(CVA)
  3. Vol.5

Sparse Isotropic Hashing

https://ipsj.ixsq.nii.ac.jp/records/94695
https://ipsj.ixsq.nii.ac.jp/records/94695
2745a72a-767e-4b4d-a22b-bb2cdd378317
名前 / ファイル ライセンス アクション
IPSJ-TCVA0500007.pdf IPSJ-TCVA0500007.pdf (376.9 kB)
Copyright (c) 2013 by the Information Processing Society of Japan
オープンアクセス
Item type Trans(1)
公開日 2013-07-29
タイトル
タイトル Sparse Isotropic Hashing
タイトル
言語 en
タイトル Sparse Isotropic Hashing
言語
言語 eng
キーワード
主題Scheme Other
主題 [Regular Paper - Express Paper] binary codes, nearest neighbor search, local descriptors, sparse matrix
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者所属
Denso IT Laboratory, Inc.
著者所属
Denso IT Laboratory, Inc.
著者所属
Denso IT Laboratory, Inc.
著者所属(英)
en
Denso IT Laboratory, Inc.
著者所属(英)
en
Denso IT Laboratory, Inc.
著者所属(英)
en
Denso IT Laboratory, Inc.
著者名 Ikuro, Sato Mitsuru, Ambai Koichiro, Suzuki

× Ikuro, Sato Mitsuru, Ambai Koichiro, Suzuki

Ikuro, Sato
Mitsuru, Ambai
Koichiro, Suzuki

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著者名(英) Ikuro, Sato Mitsuru, Ambai Koichiro, Suzuki

× Ikuro, Sato Mitsuru, Ambai Koichiro, Suzuki

en Ikuro, Sato
Mitsuru, Ambai
Koichiro, Suzuki

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論文抄録
内容記述タイプ Other
内容記述 This paper address the problem of binary coding of real vectors for efficient similarity computations. It has been argued that orthogonal transformation of center-subtracted vectors followed by sign function produces binary codes which well preserve similarities in the original space, especially when orthogonally transformed vectors have covariance matrix with equal diagonal elements. We propose a simple hashing algorithm that can orthogonally transform an arbitrary covariance matrix to the one with equal diagonal elements. We further expand this method to make the projection matrix sparse, which yield faster coding. It is demonstrated that proposed methods have comparable level of similarity preservation to the existing methods.
論文抄録(英)
内容記述タイプ Other
内容記述 This paper address the problem of binary coding of real vectors for efficient similarity computations. It has been argued that orthogonal transformation of center-subtracted vectors followed by sign function produces binary codes which well preserve similarities in the original space, especially when orthogonally transformed vectors have covariance matrix with equal diagonal elements. We propose a simple hashing algorithm that can orthogonally transform an arbitrary covariance matrix to the one with equal diagonal elements. We further expand this method to make the projection matrix sparse, which yield faster coding. It is demonstrated that proposed methods have comparable level of similarity preservation to the existing methods.
書誌情報 IPSJ Transactions on Computer Vision and Applications (CVA)

巻 5, p. 40-44, 発行日 2013-07-29
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-6695
出版者
言語 ja
出版者 情報処理学会
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