Distance Computation Between Binary Code and Real Vector for Efficient Keypoint Matching

• Item type: Trans(1)
• 公開日: 2013-07-29
• タイトル: Distance Computation Between Binary Code and Real Vector for Efficient Keypoint Matching
• タイトル / 言語: en
• タイトル: Distance Computation Between Binary Code and Real Vector for Efficient Keypoint Matching
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Regular Paper - Express Paper] asymmetric feature, client server system, binary hashing, real vector decomposition, keypoint matching
• 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: Chubu University
• 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: Chubu University
• 著者所属(英): en
• 著者所属(英): Chubu University
• 著者所属(英): en
• 著者所属(英): Denso IT Laboratory, Inc.
• 著者所属(英): en
• 著者所属(英): Denso IT Laboratory, Inc.
• 著者所属(英): en
• 著者所属(英): Denso IT Laboratory, Inc.
• 著者所属(英): en
• 著者所属(英): Chubu University
• 著者名: Yuji, Yamauchi; Mitsuru, Ambai; Ikuro, Sato; Yuichi, Yoshida; Hironobu, Fujiyoshi
• 著者名(英): Yuji, Yamauchi; Mitsuru, Ambai; Ikuro, Sato; Yuichi, Yoshida; Hironobu, Fujiyoshi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Image recognition in client server system has a problem of data traffic. However, reducing data traffic gives rise to worsening of performance. Therefore, we represent binary codes as high dimensional local features in client side, and represent real vectors in server side. As a result, we can suppress the worsening of the performance, but it problems of an increase in the computational cost of the distance computation and a different scale of norm between feature vectors. Therefore, to solve the first problem, we optimize the scale factor so as to absorb the scale difference of Euclidean norm. For second problem, we compute efficiently the Euclidean distance by decomposing the real vector into weight factors and binary basis vectors. As a result, the proposed method achieves the keypoint matching with high-speed and high-precision even if the data traffic was reduced.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Image recognition in client server system has a problem of data traffic. However, reducing data traffic gives rise to worsening of performance. Therefore, we represent binary codes as high dimensional local features in client side, and represent real vectors in server side. As a result, we can suppress the worsening of the performance, but it problems of an increase in the computational cost of the distance computation and a different scale of norm between feature vectors. Therefore, to solve the first problem, we optimize the scale factor so as to absorb the scale difference of Euclidean norm. For second problem, we compute efficiently the Euclidean distance by decomposing the real vector into weight factors and binary basis vectors. As a result, the proposed method achieves the keypoint matching with high-speed and high-precision even if the data traffic was reduced.
• 書誌情報: IPSJ Transactions on Computer Vision and Applications （CVA）; 巻 5, p. 124-128, 発行日 2013-07-29
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会