Fusion of Entropy-Based Color Space Selection and Statistical Color Features for Ripeness Classification of Guavas

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

Abstract

This paper presents a novel and non-destructive approach to the color appearance characterization and classification of guava ripeness. Guava ripeness is modeled using extracted statistical color features and support vector machines (SVM) are adopted to perform the classification task. Also, the role of different color spaces in entropy calculation for estimating resolving power in the characterization of ripeness levels of guava is investigated. This approach is applied to 270 guava images from three types of ripeness, i.e., under ripe, ripe, and over ripe. Entropy-based color space selection is carried out using nonparametric Kruskal–Wallis procedure. Statistical curve-fitting color features are derived from the histogram of selected color space. Experimental results show that in spite of the complexity and high variability in color appearance of guava, the modeling of guava images with statistical color curve-fitting parameters allows the capture of differentiating color features between the guava ripeness levels. The classification accuracy using six normpdf curve-fitting parameters (mean, sigma, mean_LB, mean_UB, sigma_LB, sigma_UB) is 90.37 % for testing data.

Keywords

Statistical color features Entropy Curve fitting SVM 

References

  1. 1.
    Mendoza, F., Dejmek, P., Aguilera, J.M.: Calibrated color measurements of agricultural foods using image analysis. Postharvest Biol. Technol. 41(3), 285–295 (2006)CrossRefGoogle Scholar
  2. 2.
    Abdullah, M.Z., Guan, L.C., Lim, K.C., Karim, A.A.: The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. J. Food Eng. 61, 125–135 (2004)CrossRefGoogle Scholar
  3. 3.
    Du, C., Sun, D.-W.: Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Technol. 15, 230–249 (2004)CrossRefGoogle Scholar
  4. 4.
    Hatcher, D.W., Symons, S.J., Manivannan, U.: Developments in the use of image analysis for the assessment of oriental noodle appearance and color. J. Food Eng. 61, 109–117 (2004)CrossRefGoogle Scholar
  5. 5.
    Kumar, S., Mittal, G.S.: Rapid detection of microorganisms using image processing parameters and neural network. Food Bioprocess Technol. 3(5), 741–751 (2009)CrossRefGoogle Scholar
  6. 6.
    Segnini, S., Dejmek, P., Oste, R.: A low cost video technique for color measurement of potato chips. LWT Food Sci. Technol. 32, 216–222 (1999)CrossRefGoogle Scholar
  7. 7.
    Villegas, M., Paredes, R.: Face recognition in color using complex and hyper complex representations. In: Proceedings of the 3rd Iberian Conference on Pattern Recognition and Image Analysis, Part I, vol. 68, pp. 217–224, Girona, Spain, June 2007Google Scholar
  8. 8.
    Sangwine, S.J., Ell, T.A.: Hyper complex auto- and cross-correlation of color images. In: Proceedings of the IEEE International Conference on Image Processing, vol. 2428, pp. 319–322, Kobe, Japan, Oct 1999Google Scholar
  9. 9.
    Blasco, J., Cubero, S., Gmez-Sanchis, J., Mira, P., Molt, E.: Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J. Food Eng. 90, 27–34 (2009)CrossRefGoogle Scholar
  10. 10.
    Xiaobo, Z., Jiewen, Z., Yanxiao, L.: Apple color grading based on organization feature parameters. Pattern Recogn. Lett. 28, 2046–2053 (2007)CrossRefGoogle Scholar
  11. 11.
    Mokji, M.M., Abu Bakar, S.A.R.: Starfruit classification based on linear hue computation. Elektrika 9(2), 14–19 (2007)Google Scholar
  12. 12.
    Amirulah, R., Mokji, M.M., Ibrahim, Z.: Starfruit color maturity classification using Cr as feature. In: 2010 6th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp.93–97, 15–18 Dec 2010Google Scholar
  13. 13.
    Strokes, M., Anderson, M., Chandrrsshekar, S., Motta, R.: A standard default color space for the internet: sRGB. Available at: http://www.color.org/sRGB.xalter. Accessed June 2010 (1996)

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Symbiosis International UniversityLavale, PuneIndia
  2. 2.College of EngineeringPandharpur, SolapurIndia

Personalised recommendations