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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Bibliographic Details
Published in:Electronics letters Vol. 52; no. 24; pp. 1988 - 1990
Main Authors: Xu, Yan, Wang, Quanwei, Wei, Zhenyu, Ma, Shuo
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 24.11.2016
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2016.2299