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Hierarchical Graph Convolutional Networks for Image Classification

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14196))

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Abstract

Graph-based image representation is a promising research direction that can capture the structure and semantics of images. However, existing methods for converting images to graphs often fail to preserve the hierarchical information of the image elements and produce sub-optimal or poor regions. To address these limitations, we propose a novel approach that uses a hierarchical image segmentation technique to generate graphs at multiple segmentation scales, capturing the hierarchical relationships between image elements. We also propose and train a Hierarchical Graph Convolutional Network for Image Classification (HGCIC) model that leverages the hierarchical information with three different adjacency setups on the CIFAR-10 database. Experimental results show that the proposed approach can achieve competitive or superior performance compared to other state-of-the-art methods while using smaller graphs.

Code is available at: https://github.com/IMScience-PPGINF-PucMinas/HGCIC.

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Acknowledgements

The authors would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq - (Universal 407242/2021-0 and PQ 306573/2022-9), and Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG - (Grants PPM- 00006-18). This study was also financed in part by PUC Minas and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. Image and Multimedia Data Science Laboratory (IMSCIENCE), Pontifícia Universidade Católica de Minas Gerais (PUC Minas) Belo Horizonte, Minas Gerais, Brazil

    João Pedro Oliveira Batisteli, Silvio Jamil Ferzoli Guimarães & Zenilton Kleber Gonçalves do Patrocínio Júnior

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  1. João Pedro Oliveira Batisteli
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  2. Silvio Jamil Ferzoli Guimarães
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  3. Zenilton Kleber Gonçalves do Patrocínio Júnior
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Corresponding author

Correspondence to João Pedro Oliveira Batisteli .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Batisteli, J.P.O., Guimarães, S.J.F., do Patrocínio Júnior, Z.K.G. (2023). Hierarchical Graph Convolutional Networks for Image Classification. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_5

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  • DOI: https://doi.org/10.1007/978-3-031-45389-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45388-5

  • Online ISBN: 978-3-031-45389-2

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