Learning a non‐linear combination of Mahalanobis distances using statistical inference for similarity measure

Learning a non‐linear combination of Mahalanobis distances using statistical inference for similarity measure

In this study, the authors learn a similarity measure that discriminates between inter‐class and intra‐class samples based on a statistical inference perspective. A non‐linear combination of Mahalanobis is proposed to reflect the properties of a likelihood ratio test. Since an object's appearan...

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Bibliographic Details
Main Authors: Eslam Mostafa, Asem M. Ali, Aly A. Farag
Format: Article
Language:English
Published: Wiley 2015-08-01
Series:IET Computer Vision
Subjects:
statistical inference
Mahalanobis distance nonlinear combination
interclass sample
intraclass sample
likelihood ratio test
feature vector representation
Online Access:https://doi.org/10.1049/iet-cvi.2014.0011

Internet

https://doi.org/10.1049/iet-cvi.2014.0011

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