FPGA implementation of Naive Bayes classifier for visual object recognition

Meng, Hongying and Appiah, Kofi and Hunter, Andrew and Dickinson, Patrick (2011) FPGA implementation of Naive Bayes classifier for visual object recognition. In: Seventh IEEE Workshop on Embedded Computer Vision, 20-25 June 2011, Colorado Springs.

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Abstract

In this paper, a Naive Bayes classifier was simplified and implemented as a multi-class classification tool for binary feature vectors. It was designed on FPGA using very limited hardware resources and runs quickly and efficiently in both training and testing phases. It was first tested on a handwriting digital number dataset, and then applied in the visual object recognition on a single FPGA based visual surveillance system. It was compared with a binary Self Organizing Map (bSOM) using tri-states operation on FPGA, and the experimental results demonstrated both its higher performance and lower resource usage on the FPGA chip.

Item Type: Conference or Workshop Item (Paper)
Additional Information: In this paper, a Naive Bayes classifier was simplified and implemented as a multi-class classification tool for binary feature vectors. It was designed on FPGA using very limited hardware resources and runs quickly and efficiently in both training and testing phases. It was first tested on a handwriting digital number dataset, and then applied in the visual object recognition on a single FPGA based visual surveillance system. It was compared with a binary Self Organizing Map (bSOM) using tri-states operation on FPGA, and the experimental results demonstrated both its higher performance and lower resource usage on the FPGA chip.
Keywords: FPGA, Bayes Classifier, Recognition
Subjects: G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions: College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User: Kofi Appiah
Date Deposited: 08 Apr 2011 18:18
Last Modified: 12 Oct 2012 10:50
URI: http://eprints.lincoln.ac.uk/id/eprint/4381

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