Naïve Bayesian classifier for human shape recognition

Author

Mahmud, A.R. ; Tahir, Nooritawati Md

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia

fYear

2013

fDate

8-10 March 2013

Firstpage

219

Lastpage

223

Abstract

The aim of this study is to investigate the potential of Radon Transform and Regularized Principal Component Analysis as feature extraction for classification of pedestrian, non-pedestrian and vehicles. Several classification techniques are evaluated and verified based on accuracy, specificity and computational time. Initial findings showed that the best classification technique is Naïve Bayesian along with Gaussian as kernel with 100% accuracy and execution time of 0.016s respectively for human/vehicles classification while for pedestrian/non-pedestrian classifications are 97% respectively.

Keywords

Gaussian processes; Radon transforms; belief networks; feature extraction; image classification; object recognition; principal component analysis; Gaussian technique; Radon transform; classification technique; feature extraction; human classification; human shape recognition; naive Bayesian classifier; nonpedestrian classification; pedestrian classification; regularized principal component analysis; vehicle classification; Accuracy; Bayes methods; Feature extraction; Niobium; Principal component analysis; Transforms; Vehicles; Bayesian Regularization; Levenberg Marquardt; Naïve Bayesian; Principal Component Analysis; Radon Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and its Applications (CSPA), 2013 IEEE 9th International Colloquium on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4673-5608-4

Type

conf

DOI

10.1109/CSPA.2013.6530045

Filename

6530045