Abstract
In the last decades, computer vision tasks such as face recognition, pattern classification, and object detection, have been extensively explored due to advances in computing technologies, learning algorithms, and availability of large amounts of labelled data. Image texture analysis is helpful and important to extracting valuable features of images in order to perform the aforementioned tasks. Markov Random Field (MRF) theory defines probabilistic relationships between the pixel value and its neighbouring pixels, which can help to produce features for classification purposes with deep learning. Convolutional Neural Network (CNN) is a popular feed-forward deep neural network, especially for handling visual tasks. Therefore, this study focuses on combining MRF and CNN in order to achieve hyper/multispectral image classification tasks. MRF images were generated to produce prefixed MRF filters for the first and/or second attention-like layers of CNNs to better extract features. Then CNNs were trained with the prefixed filters for experimentation on the UoM, BONN, and Cassava datasets. Experimental results have demonstrated that such combination of MRF and CNN can enhance classification accuracy while being more time-efficient for hyper/multispectral image classification compared to CNN structures without prefixed MRF filters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hung, C.-C., Song, E., Lan, Y.: Image Texture Analysis. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-13773-1
Armi, L., Fekri-Ershad, S.: Texture image analysis and texture classification methods – A review. Int. Online J. Image Process. Pattern Recognit. 2, 1–29 (2019). https://doi.org/10.48550/arXiv.1904.06554
Hammersley, J.M., Clifford, P.: Markov Fields on Finite Graphs and Lattices. Unpubl. Manuscr. 1–26 (1971)
Besag, J.: Spatial interaction and the statistical analysis of lattice systems. J. R. Stat. Soc. Ser. B 36, 192–225 (1974). https://doi.org/10.1111/j.2517-6161.1974.tb00999.x
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, London, London (2009). https://doi.org/10.1007/978-1-84800-279-1
Sahin, H.M., Miftahushudur, T., Grieve, B., Yin, H.: Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-Net. Comput. Electron. Agric. 211, 107956 (2023). https://doi.org/10.1016/j.compag.2023.107956
Li, Y., Zhang, H., Shen, Q.: Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9, 67 (2017). https://doi.org/10.3390/rs9010067
AlSuwaidi, A., Grieve, B., Yin, H.: Towards spectral-texture approach to hyperspectral image analysis for plant classification. In: Yin, H., et al. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2017, pp. 251–260. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_28
Alsuwaidi, A., Grieve, B., Yin, H.: Combining spectral and texture features in hyperspectral image analysis for plant monitoring. Meas. Sci. Technol. 29, 104001 (2018). https://doi.org/10.1088/1361-6501/aad642
Peng, Y., Yin, H.: Markov random field based convolutional neural networks for image classification. In: Yin, H., et al. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 387–396. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_42
Peng, Y., Hankins, R., Yin, H.: Data-independent feature learning with Markov random fields in convolutional neural networks. Neurocomputing 378, 24–35 (2020). https://doi.org/10.1016/j.neucom.2019.03.107
Geng, L., Sun, J., Xiao, Z., Zhang, F., Wu, J.: Combining CNN and MRF for road detection. Comput. Electr. Eng. 70, 895–903 (2018). https://doi.org/10.1016/j.compeleceng.2017.11.026
Alsuwaidi, A., Veys, C., Hussey, M., Grieve, B., Yin, H.: Hyperspectral selection based algorithm for plant classification. In: IST 2016 – 2016 IEEE International Conference Imaging System Techniques Proceedings, pp. 395–400 (2016). https://doi.org/10.1109/IST.2016.7738258
Alsuwaidi, A., Veys, C., Hussey, M., Grieve, B., Yin, H.: Hyperspectral feature selection ensemble for plant classification. In: Hyperspectral Imaging Applications (HSI 2016) (2016)
AlSuwaidi, A., Grieve, B., Yin, H.: Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(4), 1041–1055 (2018). https://doi.org/10.1109/JSTARS.2017.2788426
Peng, Y., et al.: Early detection of plant virus infection using multispectral imaging and spatial–spectral machine learning. Sci. Rep. 12, 3113 (2022). https://doi.org/10.1038/s41598-022-06372-8
Peng, Y., Dallas, M.M., Ascencio-Ibáñez, J.T., Hoyer, J.S., Legg, J., Hanley-Bowdoin, L., Grieve, B., Yin, H.: Cassava-TME204-UCBSV multispectral imaging dataset for early detection of virus infection with spatial-spectral machine learning. Zenodo (2021). https://doi.org/10.5281/zenodo.4636968
Cross, G.R., Jain, A.K.: Markov random field texture models. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5(1), 25–39 (1983). https://doi.org/10.1109/TPAMI.1983.4767341
Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29, 2352–2449 (2017). https://doi.org/10.1162/neco_a_00990
Szirányi, T., Kriston, A., Majdik, A., Tizedes, L.: Fusion markov random field image segmentation for a time series of remote sensed images. In: Faragó, I., Izsák, F., Simon, P.L. (eds.) Progress in Industrial Mathematics at ECMI 2018, pp. 621–629. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-27550-1_79
Patil, A., Rane, M.: Convolutional neural networks: an overview and its applications in pattern recognition. Smart Innov. Syst. Technol. 195, 21–30 (2021). https://doi.org/10.1007/978-981-15-7078-0_3
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2323 (1998). https://doi.org/10.1109/5.726791
Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2921–2926 (2017). https://doi.org/10.1109/IJCNN.2017.7966217
Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images (2009)
Sahin, H.M., Grieve, B., Yin, H.: Automatic multispectral image classification of plant virus from leaf samples. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2020: 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, pp. 374–384. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-62362-3_33
Acknowledgements
Halil Mertkan Sahin would also like to acknowledge the Scholarship provided by the Ministry of National Education of the Republic of Türkiye.
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
Sahin, H.M., Grieve, B., Yin, H. (2023). Combining of Markov Random Field and Convolutional Neural Networks for Hyper/Multispectral Image Classification. In: Quaresma, P., Camacho, D., Yin, H., Gonçalves, T., Julian, V., Tallón-Ballesteros, A.J. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2023. IDEAL 2023. Lecture Notes in Computer Science, vol 14404. Springer, Cham. https://doi.org/10.1007/978-3-031-48232-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-48232-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48231-1
Online ISBN: 978-3-031-48232-8
eBook Packages: Computer ScienceComputer Science (R0)