Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition

Lunbo Li; Guangfu Ma

doi:10.1007/978-3-540-85984-0_12

Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition

Lunbo Li^*
, Guangfu Ma

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

This paper presents a fast version of probabilistic neural network model for the recognition of traffic signs. The model incorporates the J-means algorithm to select the pattern layer centers and Particle Swarm Optimization (PSO) to optimize the spread parameter, enhancing its performance. In order to cope with the degradations, the Combined Blur-Affine Invariants (CBAIs) are adopted to extract the features of traffic sign symbols without any restorations which usually need a great amount of computations. The experimental results indicate that the fast version of PNN optimized using PSO is not only parsimonious but also has better generalization performance.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
Pages	90-98
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-540-85984-0_12
State	Published - 2008
Event	4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, China Duration: 15 Sep 2008 → 18 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5227 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Intelligent Computing, ICIC 2008
Country/Territory	China
City	Shanghai
Period	15/09/08 → 18/09/08

Keywords

Combined blur-affine invariants
Particle swarm optimization
Probabilistic neural networks
Traffic signs recognition

Access to Document

10.1007/978-3-540-85984-0_12

Cite this

Li, L., & Ma, G. (2008). Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings (pp. 90-98). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5227 LNAI). https://doi.org/10.1007/978-3-540-85984-0_12

Li, Lunbo ; Ma, Guangfu. / Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition. Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. pp. 90-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition",

abstract = "This paper presents a fast version of probabilistic neural network model for the recognition of traffic signs. The model incorporates the J-means algorithm to select the pattern layer centers and Particle Swarm Optimization (PSO) to optimize the spread parameter, enhancing its performance. In order to cope with the degradations, the Combined Blur-Affine Invariants (CBAIs) are adopted to extract the features of traffic sign symbols without any restorations which usually need a great amount of computations. The experimental results indicate that the fast version of PNN optimized using PSO is not only parsimonious but also has better generalization performance.",

keywords = "Combined blur-affine invariants, Particle swarm optimization, Probabilistic neural networks, Traffic signs recognition",

author = "Lunbo Li and Guangfu Ma",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Li, L & Ma, G 2008, Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition. in Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5227 LNAI, pp. 90-98, 4th International Conference on Intelligent Computing, ICIC 2008, Shanghai, China, 15/09/08. https://doi.org/10.1007/978-3-540-85984-0_12

Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition. / Li, Lunbo; Ma, Guangfu.
Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. p. 90-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5227 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper presents a fast version of probabilistic neural network model for the recognition of traffic signs. The model incorporates the J-means algorithm to select the pattern layer centers and Particle Swarm Optimization (PSO) to optimize the spread parameter, enhancing its performance. In order to cope with the degradations, the Combined Blur-Affine Invariants (CBAIs) are adopted to extract the features of traffic sign symbols without any restorations which usually need a great amount of computations. The experimental results indicate that the fast version of PNN optimized using PSO is not only parsimonious but also has better generalization performance.

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Li L, Ma G. Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings. 2008. p. 90-98. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-85984-0_12

Optimizing the performance of probabilistic neural networks using PSO in the task of traffic sign recognition

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