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• W2138037710 abstract "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." @default.
• W2138037710 created "2016-06-24" @default.
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• W2138037710 date "2013-01-01" @default.
• W2138037710 modified "2023-10-06" @default.
• W2138037710 title "Distance Computation Between Binary Code and Real Vector for Efficient Keypoint Matching" @default.
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• W2138037710 doi "https://doi.org/10.2197/ipsjtcva.5.124" @default.
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