Gegenbauer-Based Image Descriptors for Visual Scene Recognition

  • Antonio Herrera-Acosta
  • A. Rojas-DomínguezEmail author
  • J. Martín Carpio
  • Manuel Ornelas-Rodríguez
  • Héctor Puga
Part of the Studies in Computational Intelligence book series (SCI, volume 862)


Visual scene recognition is an important problem in artificial intelligence with applications in areas such as autonomous vehicles, visually impaired people assistance, augmented reality, and many other pattern recognition areas. Visual scene recognition has been tackled in recent years by means of image descriptors such as the popular Speeded-Up Robust Features (SURF) algorithm. The problem consists in analyzing the scenes in order to produce a compact representation based on a set of so called regions of interest (ROIs) and then finding the largest number of matches among a dataset of reference images that include non-affine transformations of the scenes. In this paper, a new form of descriptors based on moment invariants from Gegenbauer orthogonal polynomials is presented. Their computation is efficient and the produced feature vector is compact, containing only a couple dozens of values. Our proposal is compared against SURF by means of the recognition rate computed on a set of two hundred scenes containing challenging conditions. The experimental results show no statistically significant difference between the performances of the descriptors.


Local image descriptors Orthogonal polynomials Gegenbauer polynomials Visual scene recognition Image moment invariants 



This work was partially supported by the National Council of Science and Technology (CONACYT) of Mexico, through Grant numbers: 416924 (A. Herrera) and CATEDRAS-2598 (A. Rojas).


  1. 1.
    Chen, C.H.: Handbook of Pattern Recognition and Computer Vision. World Scientific (2016)Google Scholar
  2. 2.
    Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities. Morgan Kaufmann (2005)Google Scholar
  3. 3.
    Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Rob. 1147–1163 (2015)CrossRefGoogle Scholar
  4. 4.
    Krig, S.: Computer Vision Metrics. Springer (2016)Google Scholar
  5. 5.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Tola, E., Lepetit, V., Fua, P.: DAISY: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)CrossRefGoogle Scholar
  7. 7.
    Bai, Y., Guo, L., Jin, L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: 16th IEEE International Conference on Image Processing (ICIP) (2009)Google Scholar
  8. 8.
    Hosny, K.M.: New set of Gegenbauer moment invariants for pattern recognition applications. Arab. J. Sci. Eng. 39(10), 7097–7107 (2014)CrossRefGoogle Scholar
  9. 9.
    Zhu, H., Shu, H., Zhou, J., Lou, L., Coatrieux, J.: Image analysis by discrete orthogonal dual Hahn moments. Pattern Recogn. Lett. 28(13), 1688–1704 (2007)CrossRefGoogle Scholar
  10. 10.
    Chiang, A., Liao, S., Lu, Q., Pawlak, M.: Gegenbauer moment-based applications for chinese character recognition. In: 2002 IEEE Canadian Conference on Electrical & Computer Engineering (2002)Google Scholar
  11. 11.
    Pawlak, M.: Image Analysis by Moments: Reconstruction and Computacional Aspects. Oficyna Wydawnicza Politechniki Wroclawskiej, Wroclaw (2006)zbMATHGoogle Scholar
  12. 12.
    Fulsser, J., Suk, T., Zitová, B.: Moments and Moment Invariants in Pattern Recognition. Wiley (2009)Google Scholar
  13. 13.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Thomson (2008)Google Scholar
  14. 14.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall (2002)Google Scholar
  15. 15.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Antonio Herrera-Acosta
    • 1
  • A. Rojas-Domínguez
    • 1
    Email author
  • J. Martín Carpio
    • 1
  • Manuel Ornelas-Rodríguez
    • 1
  • Héctor Puga
    • 1
  1. 1.Tecnológico Nacional de México—Instituto Tecnológico de LeónLeónMexico

Personalised recommendations