<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors
m<sub>p</sub>-dissimilarity is a recently proposed data-dependence similarity measure. In the literature, how m<sub>p</sub>-dissimilarity is generally used for matching local image descriptors has been formalized, and three matching strategies have been proposed by incorporat...
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doaj-3933e22b1c4d4ba1835c66af589ccaf12021-03-29T21:14:33ZengIEEEIEEE Access2169-35362018-01-016553155532510.1109/ACCESS.2018.28727298476570<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image DescriptorsGuohua Lv0https://orcid.org/0000-0003-3550-8026College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Chinam<sub>p</sub>-dissimilarity is a recently proposed data-dependence similarity measure. In the literature, how m<sub>p</sub>-dissimilarity is generally used for matching local image descriptors has been formalized, and three matching strategies have been proposed by incorporating ℓ<sub>p</sub>-norm distance and m<sub>p</sub>-dissimilarity. Each of these three matching strategies is essentially a two-round matching process that utilizes ℓ<sub>p</sub>-norm distance and m<sub>p</sub>-dissimilarity individually. This paper presents two novel similarity measures for matching local image descriptors. The first similarity measure normalizes and weights the similarities that are calculated using ℓ<sub>p</sub>-norm distance and m<sub>p</sub>-dissimilarity, respectively. The second similarity measure involves a novel calculation that takes into account both spatial distance and data distribution between descriptors. The proposed similarity measures are extensively evaluated on a few image registration benchmark data sets. Experimental results will demonstrate that the proposed similarity measures achieve higher matching accuracy and are able to attain better recall results when registering multi-modal images compared with the existing matching strategies that combine ℓ<sub>p</sub>-norm distance and ℓ<sub>p</sub>-dissimilarity.https://ieeexplore.ieee.org/document/8476570/Similarity measure<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ℓ</italic><italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-norm distance<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">m</italic><italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-dissimilaritylocal descriptorsaccuracyimage registration |
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Guohua Lv <inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors IEEE Access Similarity measure <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ℓ</italic><italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-norm distance <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">m</italic><italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-dissimilarity local descriptors accuracy image registration |
author_facet |
Guohua Lv |
author_sort |
Guohua Lv |
title |
<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors |
title_short |
<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors |
title_full |
<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors |
title_fullStr |
<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors |
title_full_unstemmed |
<inline-formula> <tex-math notation="LaTeX">$\ell m_p$ </tex-math></inline-formula>: A Novel Similarity Measure for Matching Local Image Descriptors |
title_sort |
<inline-formula> <tex-math notation="latex">$\ell m_p$ </tex-math></inline-formula>: a novel similarity measure for matching local image descriptors |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
m<sub>p</sub>-dissimilarity is a recently proposed data-dependence similarity measure. In the literature, how m<sub>p</sub>-dissimilarity is generally used for matching local image descriptors has been formalized, and three matching strategies have been proposed by incorporating ℓ<sub>p</sub>-norm distance and m<sub>p</sub>-dissimilarity. Each of these three matching strategies is essentially a two-round matching process that utilizes ℓ<sub>p</sub>-norm distance and m<sub>p</sub>-dissimilarity individually. This paper presents two novel similarity measures for matching local image descriptors. The first similarity measure normalizes and weights the similarities that are calculated using ℓ<sub>p</sub>-norm distance and m<sub>p</sub>-dissimilarity, respectively. The second similarity measure involves a novel calculation that takes into account both spatial distance and data distribution between descriptors. The proposed similarity measures are extensively evaluated on a few image registration benchmark data sets. Experimental results will demonstrate that the proposed similarity measures achieve higher matching accuracy and are able to attain better recall results when registering multi-modal images compared with the existing matching strategies that combine ℓ<sub>p</sub>-norm distance and ℓ<sub>p</sub>-dissimilarity. |
topic |
Similarity measure <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ℓ</italic><italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-norm distance <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">m</italic><italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₚ</italic>-dissimilarity local descriptors accuracy image registration |
url |
https://ieeexplore.ieee.org/document/8476570/ |
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