Parametric classification of Bingham distributions based on Grassmann manifolds

Muhammad Ali, Junbin Gao, Michael Antolovich

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)5771-5784
Number of pages14
JournalIEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Issue number12
Publication statusPublished - 24 Jun 2019


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