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Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation

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Abstract

Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texture-based features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior.

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References

  1. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  2. Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognit. 43, 387–396 (2010)

    Article  MATH  Google Scholar 

  3. Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Texture-based descriptors for writer identification and verification. Expert Syst. Appl. 40(6), 2069–2080 (2013)

    Article  Google Scholar 

  4. Bianconi, F., Ceccarelli, L., Fernádez, A., Saetta, S.A.: A sequential machine vision procedure for assessing paper impurities. Comput. Ind. 65, 325–332 (2014)

  5. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  6. Cavalin, P.R., Kapp, M.N., Martins, J., Oliveira, L.S.: A multiple feature vector framework for forest species recognition. In: 28th Annual ACM Symposium on Applied Computing, pp. 16–20, New York, NY, USA (2013)

  7. Cavalin, P.R., Sabourin, R., Suen, C.Y.: Dynamic selection approaches for multiple classifier systems. Neural Comput. Appl. 22(3–4), 673–688 (2013)

    Article  Google Scholar 

  8. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: Wld: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  9. Giacinto, G., Roli, F.: Methods for dynamic classifier selection. In: International Conference on Image Analysis and Processing, pp. 659–665. IEEE Computer Society, Washington, DC (1999)

  10. Giacinto, G., Roli, F.: Dynamic classifier selection based on multiple classifier behaviour. Pattern Recognit. 34, 1879–1881 (2001)

  11. Goldfarb, L.: What is distance and why do we need the metric model for pattern learning? Pattern Recognit. 25(4), 431–438 (1992)

    Article  Google Scholar 

  12. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, New Jersey (2008)

    Google Scholar 

  13. Haffemann, L.G., Oliveira, L.S., Cavalin, P.R.: Forest species recognition using deep convolutional neural networks. In: International Conference on Pattern Recognition, pp. 1103–1107 (2014)

  14. Haralick, R.M.: Statistical and structural approaches to texture. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  15. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Article  Google Scholar 

  16. Britto Jr, A.S., Sabourin, R., Oliveira, L.S.: Dynamic selection of classifiers—a comprehensive review. Pattern Recognit. 47(11), 3665–3680 (2014)

    Article  Google Scholar 

  17. Kapp, M., Bloot, R., Cavalin, P.R., Oliveira, L.S.: Automatic forest species recognition based on multiple feature sets. In: International Joint Conference ou Neural Networks, pp. 1296–1303 (2012)

  18. Khalid, M., Lee, E.L.Y., Yusof, R., Nadaraj, M.: Design of an intelligent wood species recognition system. Int. J. Simul. Syst. Sci. Technol. 3(9), 9–17 (2008). Special Issue on: Artificial Intelligence

    Google Scholar 

  19. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  20. Ko, A.H.R., Sabourin, R., Britto Jr, A.S.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recognit. 41(5), 1718–1731 (2008)

    Article  MATH  Google Scholar 

  21. Kumar, R., Sharma, J.D., Chanda, B.: Writer-independent off-line signature verification using surroundedness feature. Pattern Recognit. Lett. 33(3), 301–308 (2012)

    Article  Google Scholar 

  22. Kuncheva, L.I., Rodriguez, J.J.: Classifier ensemble with a random linear oracle. IEEE Trans. Knowl. Data Eng. 19(4), 500–508 (2007)

  23. Lavine, B.K., Davidson, C.E., Moores, A.J., Griffiths, P.R.: Raman spectroscopy and genetic algorithms for the classification of wood types. Appl. Spectrosc. 55(8), 960–966 (2001)

    Article  Google Scholar 

  24. Lowe, D.G.: Object recognition from local scale-invariant features. Int. Conf. Comput. Vis. 2, 1150–1157 (1999)

    Google Scholar 

  25. Martins, J.G., Oliveira, L.S., Nisgoski, S., Sabourin, R.: A database for automatic classification of forest species. Mach. Vis. Appl. 24(3), 567–578 (2013)

    Article  Google Scholar 

  26. Martins, J.G., Oliveira, L.S., Sabourin, R.: Combining textural descriptors for forest species recognition. In: 38th Annual Conference on IEEE Industrial Electronics Society, pp. 1483–1488 (2012)

  27. Matas, J., Chum, O., Martin, U., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 384–393 (2002)

  28. Nuopponen, M.H., Birch, G.M., Sykes, R.J., Lee, S.J., Stewart, D.: Estimation of wood density and chemical composition by means of diffuse reflectance mid-infrared fourier transform (drift-mir) spectroscopy. J. Agric. Food Chem. 54(1), 34–40 (2006)

    Article  Google Scholar 

  29. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)

    Article  Google Scholar 

  30. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  31. Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: 3rd International Conference on Image and Signal Processing, pp. 236–243 (2008)

  32. Paivarinta, J., Rahtu, E., Heikkila, J.: Volume local phase quantization for blur-insensitive dynamic texture classification. In: 17th Scandinavian Conference on Image analysis, pp. 360–369 (2011)

  33. Paula Filho, P.L., Oliveira, L.S., Britto Jr, A.S., Sabourin, R.: Forest species recognition using color-based features. In: 20th International Conference on Pattern Recognition (ICPR2010), pp. 4178–4181 (2010)

  34. Paula Filho, P.L., Oliveira, L.S., Nisgoski, S., Britto Jr. A.S.: Forest species recognition using macroscopic image. Mach. Vis. Appl. 25, 1019–1031 (2014)

  35. Pekalska, E., Duin, R.P.W.: Dissimilarity representations allow for building good classifiers. Pattern Recognit. Lett. 23(8), 943–956 (2002)

    Article  MATH  Google Scholar 

  36. Piuri, V., Scotti, F.: Design of an automatic wood types classification system by using fluorescence spectra. IEEE Trans. Syst. Man Cybern. Part C 40(3), 358–366 (2010)

  37. Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A., et al. (eds.) Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)

  38. Santini, S., Jain, R.: Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 871–883 (1999)

  39. Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. In: 14th International Conference on Machine Learning, pp. 322–330 (1997)

  40. Tou, J.Y., Tay, Y.H., Lau, P.Y.: One-dimensional grey-level co-occurrence matrices for texture classification. In: International Symposium on Information Technology, pp. 1–6 (2008)

  41. Tou, J.Y., Tay, Y.H., Lau, P.Y.: A comparative study for texture classification techniques on wood species recognition problem. In: International Conference on Natural Computation, pp. 8–12 (2009)

  42. Tsuchikawa, S., Hirashima, Y., Sasaki, Y., Ando, K.: Near-infrared spectroscopic study of the physical and mechanical properties of wood with meso- and micro-scale anatomical observation. Appl. Spectrosc. 59(1), 86–93 (2005)

    Article  Google Scholar 

  43. Woods, K., Kegelmeyer Jr, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 405–410 (1997)

    Article  Google Scholar 

  44. Yadav, A.R., Dewal, M.L., Anand, R.S., Gupta, S.: Classification of hardwood species using ann classifier. In: National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, pp. 1–5 (2013)

  45. Yanikoglu, B., Aptoula, E., Tirkaz, C.: Automatic plant identification from photographs. Mach. Vis. Appl. 25, 1369–1383 (2014)

    Article  Google Scholar 

  46. Yusof, R., Khalid, M., Khairuddin, A.S.M.: Fuzzy logic-based pre-classifier for tropical wood species recognition system. Mach. Vis. Appl. 24(8), 1589–1604 (2013)

  47. Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)

    Article  Google Scholar 

Download references

Acknowledgments

This research has been supported by The National Council for Scientific and Technological Development (CNPq) grants 301653/2011-9 and 471050/2013-0 and the Coordination for the Improvement of Higher Education Personnel (CAPES).

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Correspondence to L. S. Oliveira.

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Martins, J.G., Oliveira, L.S., Britto, A.S. et al. Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation. Machine Vision and Applications 26, 279–293 (2015). https://doi.org/10.1007/s00138-015-0659-0

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