Multimedia Tools and Applications

, Volume 78, Issue 14, pp 20217–20237 | Cite as

A novel fusion mixture of active experts algorithm for traffic signs recognition

  • Amir Ahangi
  • Arash Fassihozzaman Langroudi
  • Fatemeh Yazdanpanah
  • Seyed Abolghasem MirroshandelEmail author


Traffic sign recognition is an important problem in today’s applications. In this paper, by combining ensemble and active learning methods, a novel fusion mixture of active experts algorithm is proposed for this problem. The active learning algorithm is a popular method for reducing the number of samples. The primary goal of active learning is diminishing complexity, increasing the convergence rate, speeding up training process, and decreasing the cost of samples labeling. The active learning, hence, chooses informative samples to train. In addition, ensemble methods are a combination of simple classifiers for improving accuracy. Each classifier tries to learn a region of dataset better than other regions that all opinions are considered on ensemble methods as an ultimate decision. The mixture of experts is one of the most modern hybrid methods in which the training process takes a relatively long time, and it is a problem for large datasets. Our proposed Mixture of Active Experts tries to solve this problem. It decreases the training time process and increases the speed of convergence for finding optimal weights by selecting only informative samples in active learning phase. It is also applicable for online situations, in which the model should be trained continuously. The results of different experiments on German Traffic Sign Recognition Benchmark dataset demonstrate that the proposed method shows 96.69% accuracy and achieved the 6th rank among all the state of the art algorithms using smaller number (only 60%) of training samples.


Traffic sign recognition Feature descriptor Mixture of experts Active learning Neural networks 



The authors thank Sara Monji-Azad for her valuable scientific advises and Seethu Mariyam Christopher for proofreading.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Amir Ahangi
    • 1
  • Arash Fassihozzaman Langroudi
    • 1
  • Fatemeh Yazdanpanah
    • 1
  • Seyed Abolghasem Mirroshandel
    • 1
    Email author
  1. 1.Department of Computer EngineeringUniversity of GuilanRashtIran

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