Bag of Riemannian Words for Virus Classification

Masoud Faraki, Mehrtash T. Harandi

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

2 Citations (Scopus)

Abstract

The efficiency of covariance descriptors (CovDs) has been explored in several image/video categorization tasks. CovDs lie on Riemannian manifolds known as tensor manifolds. Therefore, the non-Euclidean geometry should be taken into account in devising inference methods that exploit them. In this chapter, we extend the conventional bag-of-words model from Euclidean space to non-Euclidean Riemannian manifolds. To this end, we elaborate on an intrinsic bag-of-Riemannian-words (BoRW) model, which takes into account the true geometry of tensors in obtaining its codebook and histogram. Experiments on challenging a virus texture data set show that the proposed BoRW on CovDs obtains notable improvements in discrimination accuracy, in comparison to popular bag-of-words models.

Original languageEnglish
Title of host publicationCase Studies in Intelligent Computing
Subtitle of host publicationAchievements and Trends
EditorsBiju Issac, Nauman Israr
Place of PublicationNew York USA
PublisherCRC Press
Chapter13
Pages271-283
Number of pages13
ISBN (Electronic)9781482207040
ISBN (Print)9781482207033
DOIs
Publication statusPublished - 2014
Externally publishedYes

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