Traffic sign recognition based on weighted ELM and AdaBoost

A novel multiclass AdaBoost-based extreme learning machine (ELM) ensemble algorithm is proposed, in which the weighted ELM is selected as the basic weak classifier because of its much faster learning speed and much better generalisation performance than traditional support vector machines. AdaBoost...

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Vydané v:	Electronics letters Ročník 52; číslo 24; s. 1988 - 1990
Hlavní autori:	Xu, Yan, Wang, Quanwei, Wei, Zhenyu, Ma, Shuo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 24.11.2016
Predmet:	Algorithms Classification Classifiers computational complexity German traffic sign recognition benchmark database image classification image recognition Information and communications learning (artificial intelligence) Machine learning Neural networks novel multiclass AdaBoost‐based extreme learning machine ensemble algorithm Recognition traffic engineering computing Traffic signs weighted ELM weighted ELM image classification novel multiclass AdaBoost-based extreme learning machine ensemble algorithm traffic engineering computing learning (artificial intelligence) German traffic sign recognition benchmark database image recognition computational complexity
ISSN:	0013-5194, 1350-911X, 1350-911X
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Popis
Shrnutí:	A novel multiclass AdaBoost-based extreme learning machine (ELM) ensemble algorithm is proposed, in which the weighted ELM is selected as the basic weak classifier because of its much faster learning speed and much better generalisation performance than traditional support vector machines. AdaBoost acts as an ensemble learning method of a number of weighted ELMs. Then, an ensemble strong classifier is constructed by the weighted majority vote of all the weighted ELMs. Compared with the existing ELM methods, the proposed algorithm solves the problem of how to train the weighted samples by ELM in multiclass classification directly. Experiments on the German Traffic Sign Recognition Benchmark database demonstrate that the proposed algorithm can achieve a high recognition accuracy of 99.12% with a relatively lower computational complexity than many state-of-the-art algorithms.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2016.2299