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Combining Texture and Shape Descriptors for Bioimages Classification: A Case of Study in ImageCLEF Dataset

  • Anderson Brilhador
  • Thiago P. Colonhezi
  • Pedro H. Bugatti
  • Fabrício M. Lopes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Nowadays a huge volume of data (e.g. images and videos) are daily generated in several areas. The importance of this subject has led to a new paradigm known as eScience. In this scenario, the biological image domain emerges as an important research area given the great impact that it can leads in real solutions and people’s lives. On the other hand, to cope with this massive data it is necessary to integrate into the same environment not only several techniques involving image processing, description and classification, but also feature selection methods. Hence, in the present paper we propose a new framework capable to join these techniques in a single and efficient pipeline, in order to characterize biological images. Experiments, performed with the ImageCLEF dataset, have shown that the proposed framework presented notable results, reaching up to 87.5% of accuracy regarding the plant species classification, which is highly relevant and a non-trivial task.

Keywords

image descriptors feature selection classification pattern recognition 

References

  1. 1.
    Gray, J.: Jim gray on escience: a transformed scientific method. The Fourth Paradigm: Data-intensive Scientific Discovery (2009)Google Scholar
  2. 2.
    Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iView, 1–12 (2011)Google Scholar
  3. 3.
    Peng, H.: Bioimage informatics: a new area of engineering biology. Bioinformatics 24(17), 1827–1836 (2008)CrossRefGoogle Scholar
  4. 4.
    Müller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: Experimental Evaluation in Visual Information Retrieval, vol. 32. Springer (2010)Google Scholar
  5. 5.
    Bartolini, I., Ciaccia, P., Patella, M.: Warp: Accurate retrieval of shapes using phase of fourier descriptors and time warping distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 142–147 (2005)CrossRefGoogle Scholar
  6. 6.
    da Fontoura Costa, L., Cesar Jr., R.M.: Shape analysis and classification: theory and practice, 2nd edn. CRC Press (2010)Google Scholar
  7. 7.
    Attig, A., Perner, P.: A comparison between haralick’s texture descriptor and the texture descriptor based on random sets for biological images. In: Perner, P. (ed.) MLDM 2011. LNCS, vol. 6871, pp. 524–538. Springer, Heidelberg (2011)Google Scholar
  8. 8.
    Huang, C.B., Liu, Q.: An orientation independent texture descriptor for image retrieval. In: Int. Conf. on Communic., Circ. and Systems, pp. 772–776. IEEE (2007)Google Scholar
  9. 9.
    Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)Google Scholar
  10. 10.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  11. 11.
    Lewis, D.D.: Naive (bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Gardner, M., Dorling, S.: Artificial neural networks–a review of applications in the atmospheric sciences. Atmospheric Environment 32(14-15), 2627–2636 (1998)CrossRefGoogle Scholar
  13. 13.
    Statistics, L.B., Breiman, L.: Random forests. Machine Learning, 5–32 (2001)Google Scholar
  14. 14.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
  15. 15.
    Abe, S.: Support vector machines for pattern classification. Springer (2010)Google Scholar
  16. 16.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  17. 17.
    Mucciardi, A.N., Gose, E.E.: A comparison of seven techniques for choosing subsets of pattern recognition properties. IEEE Trans. on Comp. 100(9), 1023–1031 (1971)CrossRefGoogle Scholar
  18. 18.
    Lopes, F.M., Martins Jr., D.C., Cesar Jr., R.M.: Feature selection environment for genomic applications. BMC Bioinformatics 9(1), 451 (2008)CrossRefGoogle Scholar
  19. 19.
    Lopes, F.M., de Oliveira, E.A., Cesar Jr., R.M.: Analysis of the GRNs inference by using Tsallis entropy and a feature selection approach. In: Bayro-Corrochano, E., Eklundh, J.-O. (eds.) CIARP 2009. LNCS, vol. 5856, pp. 473–480. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Lopes, F.M., Martins Jr., D.C., Barrera, J., Cesar Jr., R.M.: SFFS-MR: A floating search strategy for GRNs inference. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds.) PRIB 2010. LNCS, vol. 6282, pp. 407–418. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Pinto, S.C.D., Mena-Chalco, J.P., Lopes, F.M., Velho, L., Cesar Jr., R.M.: 3D facial expression analysis by using 2D and 3D wavelet transforms. In: ICIP, pp. 1281–1284 (2011)Google Scholar
  22. 22.
    John, G.H., Kohavi, R., Pfleger, K., et al.: Irrelevant features and the subset selection problem. In: 11th Int. Conf. on Machine Learning, pp. 121–129 (1994)Google Scholar
  23. 23.
    Hall, M.A.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)Google Scholar
  24. 24.
    Sahoo, P.K., Soltani, S., Wong, A.: A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing 41(2), 233–260 (1988)CrossRefGoogle Scholar
  25. 25.
    Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)Google Scholar
  26. 26.
    Goëau, H., Bonnet, P., Joly, A., Yahiaoui, I., Barthélémy, D., Boujemaa, N., Molino, J.: The ImageCLEF 2012 Plant Identification Task (2012)Google Scholar
  27. 27.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anderson Brilhador
    • 1
  • Thiago P. Colonhezi
    • 1
  • Pedro H. Bugatti
    • 1
  • Fabrício M. Lopes
    • 1
  1. 1.Federal University of TechnologyParanáBrazil

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