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Discriminative Learning of Local Image Descriptors (2011)

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by Matthew Brown , Gang Hua , Simon Winder
Citations:170 - 2 self
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BibTeX

@MISC{Brown11discriminativelearning,
    author = {Matthew Brown and Gang Hua and Simon Winder},
    title = {Discriminative Learning of Local Image Descriptors},
    year = {2011}
}

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Abstract

In this paper, we explore methods for learning local image descriptors from training data. We describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both linear and nonlinear transforms with dimensionality reduction, and make use of discriminant learning techniques such as Linear Discriminant Analysis (LDA) and Powell minimization to solve for the parameters. Using these techniques, we obtain descriptors that exceed state-of-the-art performance with low dimensionality. In addition to new experiments and recommendations for descriptor learning, we are also making available a new and realistic ground truth data set based on multiview stereo data.

Keyphrases

local image descriptor    index term image descriptor    discriminative learning    nonlinear transforms    state-of-the-art performance    powell minimization    realistic ground truth data    nearest-neighbor classifier    linear discriminant analysis    low dimensionality    descriptor learning    new experiment    local feature    multiview stereo data    building block    dimensionality reduction   

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