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2024 | OriginalPaper | Buchkapitel

CNN for Efficient Objects Classification with Embedded Vector Fields

verfasst von : Oluwaseyi Igbasanmi, Nikolay M. Sirakov, Adam Bowden

Erschienen in: Computing, Internet of Things and Data Analytics

Verlag: Springer Nature Switzerland

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Abstract

Classification methods use image object features to distinguish between objects and assign them to classes. In the present study we develop a convolutional neural network (CNN) optimized to classify images with embedded vector fields (VFs), generated on the solution \(\hat{u}(x,y)\) of the Poisson equation, which contains the image function in its right-hand side. The embedded VF features subject to extraction, by our CNN, are trajectories and singular points (SP), which augment the image object features. The aim of this paper is to validate that the set of augmented image features increases the separability of the image objects and improves the classification statistics. To reach the aim, we implement our CNN along with four contemporary CNNs to classify two public image databases COIL100 and ISIC2020 as well as their derivatives with embedded VFs. The obtained results are presented in the paper and confirm that embedding VFs with real and complex SPs increases the classification statistics.

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Putra, T.A., Rufaida, S.I., Leu, J.S.: Enhanced skin condition prediction through machine learning using dynamic training and testing augmentation. IEEE Access 8, 40536–40546 (2020). https://doi.org/10.1109/ACCESS.2020.2976045CrossRef

Zhao, J., Zhang, Y., He, X., Xie, P.: COVID-CT-dataset: A CT scan dataset about COVID-19, CoRR, vol. abs/2003.13865, (2020). arXiv: 2003.13865. [Online]. Available: https://arxiv.org/abs/2003.13865

Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017). https://doi.org/10.1109/CVPR.2017.243

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-zCrossRef

Kim, B., Günther, T.: Robust reference frame extraction from unsteady 2D vector fields with convolutional neural networks. In: Gleicher, M., Leitte, H., Viola, I. (Guest Eds.) Eurographics Conference on Visualization (EuroVis) 2019 V. 38, N 3, Computer Graphics Forum 2019, The Eurographics Association and John Wiley & Sons Ltd. (2019.) arXiv:1903.10255v1

Branca, A., Attolico, G., Stella, E., Distante, A.: Classification and segmentation of vector flow fields using a neural network. J. Mach. Vis. Appl. 10, 174–187 (1997)CrossRef

Berenjkoub, M., Chen, G., Gunther, T.: Vortex boundary identification using convolutional neural network. In: 2020 IEEE Visualization Conference (VIS) 2020, pp. 261–265 (2020). https://doi.org/10.1109/VIS47514.2020.00059

Ebling, J., Scheuermann, G.: Template matching on vector fields using Clifford algebra. In: Gurlebeck, K., Konke, C. (eds.) 17th International Conference on the Application of CS and Math in Architecture and Civil Engineering, Weimar, Germany, 12–14 July (2006)

10.

Cantareira, G.D., Etemad, E., Paulovich, F.V.: Exploring neural network hidden layer activity using vector fields. Information 11(9), 426 (2020). https://doi.org/10.3390/info11090426CrossRef

11.

Corpetti, T., Memin, E., Perez, P.: Extraction of singular points from dense motion fields: an analytic approach. J. Math. Imaging Vis. 19, 175–198 (2003)MathSciNetCrossRef

12.

Lucian-Miti, I., Liliana, C.: Classification of holomorphic functions as pólya vector fields via differential geometry. Mathematics 9, 1890 (2021). https://doi.org/10.3390/math9161890CrossRef

13.

Ebling, J., Scheuermann, G.: Clifford convolution and pattern matching on vector fields. IEEE Visual. 2003, 193–200 (2003). https://doi.org/10.1109/VISUAL.2003.1250372CrossRef

14.

Bina, T., Yib, L.: CNN-based flow field feature visualization method. Int. J. Performability Eng. 14(3), 434–444 (2018). https://doi.org/10.23940/ijpe.18.03.p4.434444CrossRef

15.

Tari, S., Genctav, M.: From a non-local Ambrosio-Tortorelli phase field to a randomized part hierarchy tree. J. Math. Imaging Vis. 49(1), 69–86 (2014)CrossRef

16.

Bayak, I.: Applications of the local algebras of vector fields to the modelling of physical phenomena, vol. 38 (2015). https://doi.org/10.7546/jgsp-38-2015-1-23

17.

Zhang, E., Mischaikow, K., Turk, G.: Vector field design on surfaces. ACM Trans. Graphics 25(4), 1294–1326 (2006). https://doi.org/10.1145/1183287.1183290CrossRef

18.

Skraba, P., Wang, B., Chen, G., Rosen, P.: 2D vector field simplification based on robustness. In: IEEE Pacific Visualization Symposium 2014, pp. 49–56 (2014). https://doi.org/10.1109/PacificVis.2014.17

19.

Chen, M., Sirakov, N.M.: Poisson equation solution and its gradient vector field to geometric features detection. In: Fagan, D., Martin-Vide, C., O’Neill, M., Vega-Rodriguez, M.A. (eds.) Theory and Practice of Natural Computing. Lecture Notes in Computer Science(), vol. 11324, pp. 36–48. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04070-3_3

20.

Bowden, A., Sirakov, N.M.: Active contour directed by the Poisson gradient vector field and edge tracking. J. Math. Imaging Vis. 63, 665–680 (2021). https://doi.org/10.1007/s10851-021-01017-3MathSciNetCrossRef

21.

Igbasanmi, O., Sirakov, N.M.: On the usefulness of the vector field singular points shapes for classification. Iter. J. Appl. Comput. Math. (2024). Accepted for publication. https://www.researchsquare.com/article/rs-2862010/v1

22.

Nene, S.A., et al., Columbia object image library(coil-100), Technical Report CUCS-006-96 (1996)

23.

International Skin Imaging Collaboration. SIIM-ISIC 2020 Challenge Dataset. International Skin Imaging Collaboration (2020). https://doi.org/10.34970/2020-ds01

24.

Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), pp. 4278–4284 (2017)

Titel: CNN for Efficient Objects Classification with Embedded Vector Fields
verfasst von: Oluwaseyi Igbasanmi
Nikolay M. Sirakov
Adam Bowden
Verlag: Springer Nature Switzerland
Buch: Computing, Internet of Things and Data Analytics
Print ISBN: 978-3-031-53716-5

Electronic ISBN: 978-3-031-53717-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53717-2_29

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