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mp-dissimilarity is a recently proposed data-dependence similarity measure. In the literature, how mp-dissimilarity is generally used for matching local image descriptors has been formalized, and three matching strategies have been proposed by incorporating &#x2113;p-norm distance and mp-dissimilarity. Each of these three matching strategies is essentially a two-round matching process that utilizes &#x2113;p-norm distance and mp-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 &#x2113;p-norm distance and mp-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 &#x2113;p-norm distance and &#x2113;p-dissimilarity.

$\ell m_p$ : A Novel Similarity Measure for Matching Local Image Descriptors