Feature Selection for ‘Orange Skin’ Type Surface Defect in Furniture Elements

  • Bartosz ŚwiderskiEmail author
  • Michał Kruk
  • Grzegorz Wieczorek
  • Jarosław Kurek
  • Katarzyna Śmietańska
  • Leszek J. Chmielewski
  • Jarosław Górski
  • Arkadiusz OrłowskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


The surfaces of furniture elements having the orange skin surface defect were investigated in the context of selecting optimum features for surface classification. Features selected from a set of 50 features were considered. Seven feature selection methods were used. The results of these selections were aggregated and found consistently positive for some of the features. Among them were primarily the features based on local adaptive thresholding and on Hilbert curves used to evaluate the image brightness variability. These types of features should be investigated further in order to find the features with more significance in the problem of surface quality inspection. The groups of features which appeared least profitable in the analysis were the two features based on percolation, and the one based on Otsu global thresholding.


Feature selection Surface defect Orange skin Detection Furniture Feature selection Brightness variability 


  1. 1.
    Chmielewski, L.J., Orłowski, A., Wieczorek, G., Śmietańska, K., Górski, J.: Testing the limits of detection of the orange ‘skin’ defect in furniture elements with the HOG features. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10192, pp. 276–286. Springer, Cham (2017). Scholar
  2. 2.
    Karras, D.A.: Improved defect detection using support vector machines and wavelet feature extraction based on vector quantization and SVD techniques. In: Proceedings of International Joint Conference on Neural Networks, vol. 3, pp. 2322–2327, July 2003.
  3. 3.
    Musat, E.C., Salca, E.A., Dinulica, F., et al.: Evaluation of color variability of oak veneers for sorting. BioResources 11(1), 573–584 (2016). Scholar
  4. 4.
    Konieczny, J., Meyer, G.: Computer rendering and visual detection of orange peel. J. Coat. Technol. Res. 9(3), 297–307 (2012). Scholar
  5. 5.
    Armesto, L., Tornero, J., Herraez, A., Asensio, J.: Inspection system based on artificial vision for paint defects detection on cars bodies. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1–4, May 2011.
  6. 6.
    Allard, M., Jaecques, C., Kauffer, I.: Coating material which can be thermally cured and hardened by actinic radiation and use thereof. US Patent 6,949,591, 27 September 2005Google Scholar
  7. 7.
    Bucur, V.: Techniques for high resolution imaging of wood structure: a review. Meas. Sci. Technol. 14(12), R91 (2003). Scholar
  8. 8.
    Longuetaud, F., Mothe, F., Kerautret, B., et al.: Automatic knot detection and measurements from X-ray CT images of wood: a review and validation of an improved algorithm on softwood samples. Comput. Electron. Agric. 85, 77–89 (2012). Scholar
  9. 9.
    Kruk, M., Świderski, B., Osowski, S., Kurek, J., et al.: Melanoma recognition using extended set of descriptors and classifiers. EURASIP J. Image Video Process. 2015(1) (2015).
  10. 10.
    Kurek, J., Świderski, B., Dhahbi, S., Kruk, M., et al.: Chaos theory-based quantification of ROIs for mammogram classification. In: Tavares, J.M.R.S., Natal, J.R.M. (eds.) Computational Vision and Medical Image Processing V. Proceedings of 5th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing VipIMAGE 2015, pp. 187–191. CRC Press, Tenerife, 19–21 October 2015. Scholar
  11. 11.
    Świderski, B., Osowski, S., Kurek, J., Kruk, M., et al.: Novel methods of image description and ensemble of classifiers in application to mammogram analysis. Expert Syst. Appl. 81, 67–78 (2017). Scholar
  12. 12.
    Kruk, M., Świderski, B., Śmietańska, K., Kurek, J., Chmielewski, L.J., Górski, J., Orłowski, A.: Detection of ‘orange skin’ type surface defects in furniture elements with the use of textural features. In: Saeed, K., Homenda, W., Chaki, R. (eds.) CISIM 2017. LNCS, vol. 10244, pp. 402–411. Springer, Cham (2017). Scholar
  13. 13.
    Pohjalainen, J., Räsänen, O., Kadioglu, S.: Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput. Speech Lang. 29(1), 145–171 (2015). Scholar
  14. 14.
    Chmielewski, L.J., Orłowski, A., Śmietańska, K., Górski, J., Krajewski, K., Janowicz, M., Wilkowski, J., Kietlińska, K.: Detection of surface defects of type ‘orange skin’ in furniture elements with conventional image processing methods. In: Huang, F., Sugimoto, A. (eds.) PSIVT 2015. LNCS, vol. 9555, pp. 26–37. Springer, Cham (2016). Scholar
  15. 15.
    Świderski, B., Osowski, S., Kruk, M., Kurek, J.: Texture characterization based on the Kolmogorov-Smirnov distance. Expert Syst. Appl. 42(1), 503–509 (2015). Scholar
  16. 16.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). Scholar
  17. 17.
    Pohjalainen, J.: Feature selection code (2015). Accessed 25 Apr 2017
  18. 18.
    Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: Vassilopoulos, J. (ed.) Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, pp. 388–391. IEEE Computer Society, Herndon, 5–8 November 1995.
  19. 19.
    Hall, M.A., Smith, L.A.: Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In: Proceedings of 12th International Florida AI Research Society Conference FLAIRS 1999, AAAI, 1–5 May 1999Google Scholar
  20. 20.
    Liu, H., Yu, L.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of 20th International Conference on Machine Leaning ICML2003, pp. 856–863. ICM, Washington, D.C. (2003)Google Scholar
  21. 21.
    Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: an enabling technique. Data Min. Knowl. Disc. 6(4), 393–423 (2002). Scholar
  22. 22.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)zbMATHGoogle Scholar
  23. 23.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)CrossRefGoogle Scholar
  24. 24.
    Cawley, G.C., Talbot, N.L.C.: Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. Bioinformatics 22(19), 2348–2355 (2006). Scholar
  25. 25.
    Wei, L.J.: Asymptotic conservativeness and efficiency of Kruskal-Wallis test for K dependent samples. J. Am. Stat. Assoc. 76(376), 1006–1009 (1981). Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Applied Informatics and Mathematics – WZIMWarsaw University of Life Sciences – SGGWWarsawPoland
  2. 2.Faculty of Wood Technology – WTDWarsaw University of Life Sciences – SGGWWarsawPoland

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