- Title
- Enhancing image registration performance by incorporating distribution and spatial distance of local descriptors
- Creator
- Lv, Guohua; Teng, Shyh; Lu, Guojun
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164420
- Identifier
- vital:13063
- Identifier
-
https://doi.org/10.1016/j.patrec.2018.01.008
- Identifier
- ISBN:0167-8655
- Abstract
- A data dependency similarity measure called mp-dissimilarity has been recently proposed. Unlike ℓp-norm distance which is widely used in calculating the similarity between vectors, mp-dissimilarity takes into account the relative positions of the two vectors with respect to the rest of the data. This paper investigates the potential of mp-dissimilarity in matching local image descriptors. Moreover, three new matching strategies are proposed by considering both ℓp-norm distance and mp-dissimilarity. Our proposed matching strategies are extensively evaluated against ℓp-norm distance and mp-dissimilarity on a few benchmark datasets. Experimental results show that mp-dissimilarity is a promising alternative to ℓp-norm distance in matching local descriptors. The proposed matching strategies outperform both ℓp-norm distance and mp-dissimilarity in matching accuracy. One of our proposed matching strategies is comparable to ℓp-norm distance in terms of recall vs 1-precision. © 2018 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Pattern Recognition Letters Vol. 103, no. (2018), p. 46-52
- Rights
- Copyright © 2018 Elsevier B.V.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; 1702 Cognitive Science; Euclidean distance; Local image descriptors; mp-dissimilarity; Matching; Similarity measure
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