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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Avelar, P.H., Tavares, A.R., da Silveira, T.L., Jung, C.R., Lamb, L.C.: Superpixel image classification with graph attention networks. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 203–209. IEEE (2020)
Beaini, D., Passaro, S., Létourneau, V., Hamilton, W., Corso, G., Liò, P.: Directional graph networks. In: International Conference on Machine Learning, pp. 748–758. PMLR (2021)
Bresson, X., Laurent, T.: Residual gated graph convnets. arXiv preprint arXiv:1711.07553 (2017)
Corso, G., Cavalleri, L., Beaini, D., Liò, P., Veličković, P.: Principal neighbourhood aggregation for graph nets. In: Advances in Neural Information Processing Systems, vol. 33, pp. 13260–13271 (2020)
Cousty, J., Najman, L.: Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 272–283. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21569-8_24
Cousty, J., Najman, L., Kenmochi, Y., Guimarães, S.: Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps. J. Math. Imaging Vis. 60(4), 479–502 (2018)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. J. Mach. Learn. Res. 24(43), 1–48 (2023)
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
Fey, M., Lenssen, J.E., Weichert, F., Müller, H.: SplineCNN: fast geometric deep learning with continuous b-spline kernels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 869–877 (2018)
Guigues, L., Cocquerez, J.P., Le Men, H.: Scale-sets image analysis. Int. J. Comput. Vision 68(3), 289–317 (2006)
Guimarães, S., Kenmochi, Y., Cousty, J., Patrocinio, Z., Najman, L.: Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity. Math. Morphol.-Theory Appl. 2(1), 55–75 (2017)
Han, K., Wang, Y., Guo, J., Tang, Y., Wu, E.: Vision GNN: an image is worth graph of nodes. In: NeurIPS (2022)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Johnson, J., et al.: Image retrieval using scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3668–3678 (2015)
Knyazev, B., Lin, X., Amer, M., Taylor, G.: Image classification with hierarchical multigraph networks. In: Sidorov, K., Hicks, Y. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 223.1-223.13. BMVA Press (2019). https://doi.org/10.5244/C.33.223
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
Liu, Q., Xiao, L., Yang, J., Wei, Z.: CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(10), 8657–8671 (2020)
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5115–5124 (2017)
Rampášek, L., Galkin, M., Dwivedi, V.P., Luu, A.T., Wolf, G., Beaini, D.: Recipe for a general, powerful, scalable graph transformer. In: Advances in Neural Information Processing Systems, vol. 35, pp. 14501–14515 (2022)
Vasudevan, V., Bassenne, M., Islam, M.T., Xing, L.: Image classification using graph neural network and multiscale wavelet superpixels. Pattern Recogn. Lett. (2023)
Wan, S., Gong, C., Zhong, P., Du, B., Zhang, L., Yang, J.: Multiscale dynamic graph convolutional network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(5), 3162–3177 (2019)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2020)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-45389-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45388-5
Online ISBN: 978-3-031-45389-2
eBook Packages: Computer ScienceComputer Science (R0)