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A New Algorithm to Generate Image Sets for Classification and Forecasting Problems

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1410)

Abstract

Image recognition is a well-known and currently addressed problem in Artificial Intelligence. It has many practical applications, including medical image analysis, face recognition, food image analysis and image retrieval, among others. One of the main problems that arises to obtain a model for classification and forecasting is the need to have a sufficiently large set of images to be able to train the model used and subsequently validate it. The search and sorting of the images required to obtain this set is time consuming. In this work, an algorithm is presented that, from a set of images, performs different transformations in terms of shape, color, transparency, perspective, and focus, so that the set of available images is significantly increased and allows training and model validation to be carried out. Considering the proposed transformations, the construction time of the image sets is significantly reduced, and the training model is provided with relevant information for its optimization.

Keywords

  • Algorithm
  • Image
  • Classification
  • Forecasting

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  • DOI: 10.1007/978-3-030-87687-6_2
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References

  1. Zhao, K., et al.: Application research of image recognition technology based on CNN in image location of environmental monitoring UAV. EURASIP J. Image. Video Process. 2018(1), 1–11 (2018). https://doi.org/10.1186/s13640-018-0391-6

    CrossRef  Google Scholar 

  2. Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017)

    CrossRef  Google Scholar 

  3. Eikelboom, J.A., et al.: Improving the precision and accuracy of animal population estimates with aerial image object detection. MEE 10(11), 1875–1887 (2019)

    Google Scholar 

  4. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35, 3991–4458 (2003)

    CrossRef  Google Scholar 

  5. Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2D–3D hybrid approach to automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1927–1943 (2007)

    CrossRef  Google Scholar 

  6. Mian, A.S., Bennamoun, M., Owens, R.: Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1584–1601 (2006)

    CrossRef  Google Scholar 

  7. Kim, T.-K., Kittler, J., Cipolla, R.: Discriminative learning and recognition of image set classes using canonical correlations. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1005–1018 (2007)

    CrossRef  Google Scholar 

  8. Wang, R., Shan, S., Chen, X., Gao, W.: Manifold-manifold distance with application to face recognition based on image set. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  9. Wang, R., Chen, X.: Manifold discriminant analysis. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 429–436 (2009)

    Google Scholar 

  10. Cevikalp, H., Triggs, B.: Face recognition based on image sets. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 2567–2573 (2010)

    Google Scholar 

  11. Harandi, M.T., Sanderson, C., Shirazi, S., Lovell, B.C.: Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 2705–2712 (2010)

    Google Scholar 

  12. Hu, Y., Mian, A.S., Owens, R.: Face recognition using sparse approximated nearest points between image sets. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1992–2004 (2012)

    CrossRef  Google Scholar 

  13. Javidi, B.: Image Recognition and Classification: Algorithms, Systems, and Applications. CRC Press, New York (2002). https://doi.org/10.1201/9780203910962

  14. Wu, R., Yan, S., Shan, Y., Dang, Q., Sun, G.: Deep image: scaling up image recognition. arXiv preprint arXiv:1501.02876 7(8) (2015)

  15. Memisevic, R., Hinton, G.: Unsupervised learning of image transformations. In: 2007 IEEE CVPR, pp. 1–8. IEEE (2007)

    Google Scholar 

  16. OpenCV. https://docs.opencv.org/4.1.0/. Accessed 21 May 2021

  17. Numpy. https://numpy.org/doc/1.16/. Accessed 21 May 2021

  18. Marciniak, T., Chmielewska, A., Weychan, R., Parzych, M., Dabrowski, A.: Influence of low resolution of images on reliability of face detection and recognition. Multimedia Tools Appl. 74(12), 4329–4349 (2013). https://doi.org/10.1007/s11042-013-1568-8

    CrossRef  Google Scholar 

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Correspondence to Jesús-Ángel Román-Gallego .

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Román-Gallego, JÁ., Pérez-Delgado, ML., Cunillera, R.P. (2022). A New Algorithm to Generate Image Sets for Classification and Forecasting Problems. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_2

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