Learning a non-linear combination of Mahalanobis distances using statistical inference for similarity measure
IET Computer Vision(2015)
Abstract
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 appearance is influenced by the identity of the object and variations in the capturing process, the authors repre...
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Key words
computer vision,face recognition,feature extraction,image capture,image representation,image sampling,inference mechanisms,learning (artificial intelligence),statistical analysis
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