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Parametric Classification of Bingham Distributions Based on Grassmann Manifolds.

Authors
  • Ali, Muhammad
  • Gao, Junbin
  • Antolovich, Michael
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Dec 01, 2019
Volume
28
Issue
12
Pages
5771–5784
Identifiers
DOI: 10.1109/TIP.2019.2922100
PMID: 31247550
Source
Medline
Language
English
License
Unknown

Abstract

In this paper, we present a novel Bayesian classification framework of the matrix variate Bingham distributions with the inclusion of its normalizing constant and develop a consistent general parametric modeling framework based on the Grassmann manifolds. To calculate the normalizing constants of the Bingham model, this paper extends the method of saddle-point approximation (SPA) to a new setting. Furthermore, it employs the standard theory of maximum likelihood estimation (MLE) to evaluate the involved parameters in the used probability density functions. The validity and performance of the proposed approach are tested on 14 real-world visual classification databases. We have compared the classification performance of our proposed approach with the baselines from the previous related approaches. The comparison shows that on most of the databases, the performance of our approach is superior.

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