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Kernel Local Descriptors with Implicit Rotation Matching

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Abstract

In this work we design a kernelized local feature descriptor and propose a matching scheme for aligning patches quickly and automatically. We analyze the SIFT descriptor from a kernel view and identify and reproduce some of its underlying benefits. We overcome the quantization artifacts of SIFT by encoding pixel attributes in a continuous manner via explicit feature maps. Experiments performed on the patch dataset of Brown et al. [3] show the superiority of our descriptor over methods based on supervised learning.
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Dates and versions

hal-01145656 , version 1 (25-04-2015)

Identifiers

  • HAL Id : hal-01145656 , version 1

Cite

Andrei Bursuc, Giorgos Tolias, Hervé Jégou. Kernel Local Descriptors with Implicit Rotation Matching. ACM International Conference on Multimedia Retrieval, 2015, Shanghai, China. ⟨hal-01145656⟩
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