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Local complex features learned by randomized neural networks for texture analysis

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Abstract

Texture is a visual attribute largely used in many problems of image analysis. Many methods that use learning techniques have been proposed for texture discrimination, achieving improved performance over previous handcrafted methods. In this paper, we present a new approach that combines a learning technique and the complex network (CN) theory for texture analysis. This method takes advantage of the representation capacity of CN to model a texture image as a directed network and then uses the topological information of vertices to train a randomized neural network. This neural network has a single hidden layer and uses a fast learning algorithm to learn local CN patterns for texture characterization. Thus, we use the weights of the trained neural network to compose a feature vector. These feature vectors are evaluated in a classification experiment in four widely used image databases. Experimental results show a high classification performance of the proposed method compared to other methods, indicating that our approach can be used in many image analysis problems.

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Data availability

All data included in this study are available upon request by contact with the corresponding author.

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Acknowledgements

Lucas Correia Ribas gratefully acknowledges the financial support grant #s 2023/04583-2 and 2018/22214-6, São Paulo Research Foundation (FAPESP). Jarbas Joaci de Mesquita Sá Junior thanks CNPq (National Council for Scientific and Technological Development, Brazil) (Grant: 302183/2017-5) for the financial support. Leonardo Scabini acknowledges funding from FAPESP (Grants #2019/07811-0 and #2021/09163-6) and CNPq (Grant #142438/2018-9). Odemir M. Bruno thanks the financial support of CNPq (Grant # 307897/2018-4) and FAPESP (Grant #s 18/22214-6). The authors are also grateful to the NVIDIA GPU Grant Program for the donation of the Quadro P6000 and the Titan Xp GPUs used in this research.

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Authors and Affiliations

  1. Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University, Rua Cristóvão Colombo, 2265, São José do Rio Preto, São Paulo, SP, 15054-000, Brazil

    Lucas C. Ribas

  2. Institute of Mathematics and Computer Science, University of São Paulo, Av. Trab. São Carlense, 400, São Carlos, São Paulo, 13566-590, Brazil

    Odemir M. Bruno

  3. São Carlos Institute of Physics, University of São Paulo, Av. Trab. São Carlense, 400, São Carlos, São Paulo, 13566-590, Brazil

    Leonardo F. S. Scabini & Odemir M. Bruno

  4. Curso de Engenharia da Computação Programa de Pós-Graduação em Engenharia Elétrica e de Computação, Campus de Sobral, Universidade Federal do Ceará, Rua Coronel Estanislau Frota, 563, Sobral, CE, 62010-560, Brazil

    Jarbas Joaci de Mesquita Sá Junior

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  1. Lucas C. Ribas
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Correspondence to Lucas C. Ribas.

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Ribas, L.C., Scabini, L.F.S., de Mesquita Sá Junior, J.J. et al. Local complex features learned by randomized neural networks for texture analysis. Pattern Anal Applic 27, 23 (2024). https://doi.org/10.1007/s10044-024-01230-x

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