Artificial Bee Colony Optimization for Feature Selection of Traffic Sign Recognition

Diogo L. da Silva, Leticia M. Seijas and Carmelo J. A. Bastos-Filho
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Diogo L. da Silva: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife, Brazil & Federal Institute of Pernambuco (IFPE), Palmares, Brazil
Leticia M. Seijas: Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Carmelo J. A. Bastos-Filho: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife, Brazil

International Journal of Swarm Intelligence Research (IJSIR), 2017, vol. 8, issue 2, 50-66

Abstract: This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:8:y:2017:i:2:p:50-66

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