Inverted Dirichlet and Related Distributions Based Feature Mapping for Data Classification

Md Hafizur Rahman, Nizar Bouguila

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a distribution based feature mapping technique to improve the baseline accuracy of SVM kernels in different computer vision tasks. The proposed technique is based on learning parameters from the data and use that parameters to make inference from new data. The learned parameters can be thought of as prior knowledge about the data representation. Utilizing such prior knowledge about the data distribution increases the discriminative power of the classifier. Our proposed feature mapping technique is based on inverted Dirichlet, generalized inverted Dirichlet and inverted Beta Liouville distributions. These distributions are efficient in modelling semi-bounded data which are prevalent in computer vision problems. Our experimental results demonstrate the effectiveness of the proposed method in texture recognition, natural scene recognition and human action recognition in videos.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3588-3593
Number of pages6
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
StatePublished - Oct 11 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: Oct 11 2020Oct 14 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
CountryCanada
CityToronto
Period10/11/2010/14/20

Keywords

  • Feature Mapping
  • Generalized Inverted Dirichlet Distribution
  • Inverted Beta-Liouville Distribution
  • Inverted Dirichlet Distribution
  • SVM

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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