Vehicle Logo Recognition with an Ensemble of Classifiers

  • Bogusław Cyganek
  • Michał Woźniak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8398)


The paper presents a system for vehicle logo recognition from real digital images. The process starts with license plates localization, followed by vehicle logo detection. For this purpose the structural tensor is employed which allows fast and reliable detections even in low quality images. Detected logo areas are classified to the car brands with help of the classifier operating in the multi-dimensional tensor spaces. These are obtained after the Higher-Order Singular Value Decomposition of the prototype logo tensors. The proposed method shows high accuracy and fast operation, as verified by the experiments.


Vehicle logo recognition logo classification tensor classifiers 


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  1. 1.
    Bugaj, M., Cyganek, B.: GPU Based Computation of the Structural Tensor for Real-Time Figure Detection. In: Proceedings of the 20th International Conference on Computer Graphics, Visualization and Computer Vision (WSCG 2012), Czech Republic (2012)Google Scholar
  2. 2.
    Cyganek, B.: Object Detection and Recognition in Digital Images. Theory and Practice. Wiley (2013) Google Scholar
  3. 3.
    Cyganek, B., Siebert, J.P.: An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley (2009)Google Scholar
  4. 4.
    Cyganek, B.: An Analysis of the Road Signs Classification Based on the Higher-Order Singular Value Decomposition of the Deformable Pattern Tensors. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part II. LNCS, vol. 6475, pp. 191–202. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Dai, S., Huang, H., Gao, Z.: Vehicle-logo recognition method based on Tchebichef moment invariants and SVM. In: Software Engineering, WCSE 2009, pp. 18–21 (2009)Google Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley (2001)Google Scholar
  7. 7.
    Jahne, B.: Digital Image Processing. Springer (2005)Google Scholar
  8. 8.
    Kolda, T.G., Bader, B.W.: Tensor Decompositions and Applications. SIAM Review, 455–500 (2008)Google Scholar
  9. 9.
    de Lathauwer, L.: Signal Processing Based on Multilinear Algebra. PhD dissertation, Katholieke Universiteit Leuven (1997)Google Scholar
  10. 10.
    de Lathauwer, L., Moor de, B., Vandewalle, J.: A Multilinear Singular Value Decomposition. SIAM Journal of Matrix Analysis and Applications 21(4), 1253–1278 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
  12. 12.
    Petrovic, V.S., Cootes, T.F.: Analysis of features for rigid structure vehicle type recognition. In: Proc. BMVC (2004)Google Scholar
  13. 13.
    Psyllos, A.P., Anagnostopoulos, C.-N.E., Kayafas, E.: Vehicle logo recognition using a sift-based enhanced matching scheme. IEEE ITS 11(2), 322–328 (2010)Google Scholar
  14. 14.
    Savas, B., Eldén, L.: Handwritten digit classification using higher order singular value decomposition. Pattern Recognition 40, 993–1003 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2009)Google Scholar
  16. 16.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bogusław Cyganek
    • 1
  • Michał Woźniak
    • 2
  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.Wrocław University of TechnologyWrocławPoland

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