A multiresolution wavelet networks architecture and its application to pattern recognition

This paper aims at addressing a challenging research in both fields of the wavelet neural network theory and the pattern recognition. A novel architecture of the wavelet network based on the multiresolution analysis (MRWN) and a novel learning algorithm founded on the Fast Wavelet Transform (FWTLA)...

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Bibliographic Details
Published inPattern recognition and image analysis Vol. 27; no. 3; pp. 494 - 510
Main Authors Ejbali, R., Jemai, O., Zaied, M.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.07.2017
Springer Nature B.V
Subjects
Algorithms
Analysis
Computer Science
Empirical analysis
Image Processing and Computer Vision
Machine learning
Multiresolution analysis
Neural networks
Pattern Recognition
Pattern recognition systems
Processing
Representation
Understanding of Images
Wavelet analysis
Wavelet transforms
multiresolution wavelet network
fast learning algorithm
pattern recognition
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Summary:This paper aims at addressing a challenging research in both fields of the wavelet neural network theory and the pattern recognition. A novel architecture of the wavelet network based on the multiresolution analysis (MRWN) and a novel learning algorithm founded on the Fast Wavelet Transform (FWTLA) are proposed. FWTLA has numerous positive sides compared to the already existing algorithms. By exploiting this algorithm to learn the MRWN, we suggest a pattern recognition system (FWNPR). We show firstly its classification efficiency on many known benchmarks and then in many applications in the field of the pattern recognition. Extensive empirical experiments are performed to compare the proposed methods with other approaches.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661817030105