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Forest species recognition using macroscopic images

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Abstract

The recognition of forest species is a very challenging task that generally requires well-trained human specialists. However, few reach good accuracy in classification due to the time taken for their training; then they are not enough to meet the industry demands. Computer vision systems are a very interesting alternative for this case. The construction of a reliable classification system is not a trivial task, though. In the case of forest species, one must deal with the great intra-class variability and also the lack of a public available database for training and testing the classifiers. To cope with such a variability, in this work, we propose a two-level divide-and-conquer classification strategy where the image is first divided into several sub-images which are classified independently. In the lower level, all the decisions of the different classifiers, trained with different features, are combined through a fusion rule to generate a decision for the sub-image. The higher-level fusion combines all these partial decisions for the sub-images to produce a final decision. Besides the classification system we also extended our previous database, which now is composed of 41 species of Brazilian flora. It is available upon request for research purposes. A series of experiments show that the proposed strategy achieves compelling results. Compared to the best single classifier, which is a SVM trained with a texture-based feature set, the divide-and-conquer strategy improves the recognition rate in about 9 percentage points, while the mean improvement observed with SVMs trained on different descriptors was about 19 percentage points. The best recognition rate achieved in this work was 97.77 %.

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Notes

  1. http://web.inf.ufpr.br/vri/forest-species-database-macroscopic.

References

  1. Thomas, L., Milli, L.: A robust gm-estimator for the automated detection of external defects on barked hardwood logs and stems. IEEE Trans Sig Proc 55, 3568–3576 (2007)

    Article  Google Scholar 

  2. Huber, H.A.: A computerized economic comparison of a conventional furniture rough mill with a new system of processing. For Prod J 21(2), 34–39 (1971)

    Google Scholar 

  3. Buechler DN, Misra DK (2001) Subsurface detection of small voids in low-loss solids. In: First ISA/IEEE conference sensor for industry, pp 281–284

  4. Cavalin PR, Oliveira LS, Koerich A, Britto AS (2006) Wood defect detection using grayscale images and an optimized feature set. In: 32nd Annual conference of the IEEE industrial electronics society, Paris, France, pp 3408–3412

  5. Vidal, J.C., Mucientes, M., Bugarin, A., Lama, M.: Machine scheduling in custom furniture industry through neuro-evolutionary hybridization. Appl Soft Comput 11, 1600–1613 (2011)

    Article  Google Scholar 

  6. Puiri, 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)

    Article  Google Scholar 

  7. Paula PL, Oliveira LS, Britto AS, Sabourin R (2010) Forest species recognition using color-based features. Int Conf Pattern Recognit, pp 4178–4181

  8. 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 

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

    Article  Google Scholar 

  10. Orton, C.R., Parkinson, D.Y., Evans, P.D.: Fourier transform infrared studies of heterogeneity, photodegradation, and lignin/hemicellulose ratios within hardwoods and softwoods. Appl Spectrosc 583, 1265–1271 (2004)

    Article  Google Scholar 

  11. 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, 960–966 (2001)

    Article  Google Scholar 

  12. Martins, J., 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 

  13. Zavaschi, T., Oliveira, L.S., Souza Jr, A.B., Koerich, A.: Fusion of feature sets and classifiers for facial expression recognition. Expert Syst Appl 40(2), 646–655 (2013)

    Article  Google Scholar 

  14. Cavalin PR, Oliveira LS, Koerich A, Britto AS (2012) Combining textural descriptors for forest species recognition. In: 32nd Annual conference of the IEEE industrial electronics society, Montreal, Canada

  15. Tou JY, Lau PY, Proceedings of Y. H. Tay (2007) Computer vision-based wood recognition system. In: International workshop on advanced image technology

  16. Tou JY, Tay YH, Lau PY (2008) One-dimensional grey-level co-occurrence matrices for texture classification. In: International symposium on information technology (ITSim 2008), pp 1–6

  17. Tou, J.Y., Tay, Y.H., Lau, P.Y.: A comparative study for texture classification techniques on wood species recognition problem. Int Conf Nat Comput 5, 8–12 (2009)

    Google Scholar 

  18. Khalid M, Lee ELY, Yusof R, Nadaraj M (2008) Design of an intelligent wood species recognition system. IJSSST 9(3)

  19. Nasirzadeh M, Khazael AA, Khalid MB (2010) Woods recognition system based on local binary pattern. In: Second international conference on computational intelligence, communication systems and networks, pp 308–313

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

    Article  Google Scholar 

  21. Kuncheva L (2004) Combining pattern classifiers. In: Methods and Algorithms, Wiley, New York

  22. Saso, D., Zenko, B.: Is combining classifiers with stacking better than selecting the best one? Mach Learn 54(3), 255–273 (2004)

    Article  MATH  Google Scholar 

  23. Dong Y-S, Han K-S (2005) Boosting SVM classifiers by ensemble. In: Special interest tracks and posters of the 14th International Conference on World Wide Web. ACM, p 1073

  24. Rokach, L.: Ensemble-based classifiers. Artif Intell 33(1–2), 1–39 (2010)

    Article  Google Scholar 

  25. Zhu J, Steven CH, Michael R, Shuicheng Y (2008) Near duplicate keyframe retrieval by nonrigid image matching. ACM Multimedia, pp 41–50

  26. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE, 67(5)

  27. Franco, A., Nanni, L.: Fusion of classifiers for illumination robust face recognition. Expert Syst Appl 36, 8946–8954 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Ojala, T., Pietikainen, 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 

  30. Zhao, G., Ahonen, T., Matas, J., Pietikainen, M.: Rotation-invariant image and video description with local binary pattern features. IEEE Trans Image Process 21, 1465–1477 (2012)

    Article  MathSciNet  Google Scholar 

  31. Mandelbrot, B.B.: The fractal geometry of nature. W. H. Freeman and Co, New York (1982)

    MATH  Google Scholar 

  32. Chen WS, Yuan SY, Hsiao H, Hsieh CM (2001) Algorithms to estimating fractal dimension of textured images. In: IEEE international conferences on acoustics, speech and signal processing, pp 1541–1544

  33. Voss, R.F.: Characterization and measurement of random fractals. Phisica Scripta 27(13), 27–32 (1986)

    Article  Google Scholar 

  34. Melo RHC, Conci A (2008) Succolarity: defining a method to calculate this fractal measure. In: 15th International conference on systems, signals and image processing, pp 291–294

  35. Levi K, Weiss Y (2004) Learning object detection from a small number of examples. the importance of good features. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, june. 2 July 2004, vol 2, pp II-53–II-60

  36. Ojansivu V, Heikkila J (2008) Blur insensitive texture classification using local phase quantization. In: Proc. of the image and signal processing (ICISP 2008), pp 236–243

  37. Ojansivu V, Rahtu E, Heikkila J (2008) Rotation invariant local phase quantization for blur insensitive texture analysis. In: International conference on pattern recognition, pp 1–4

  38. Huang X, Li SZ, Wang Y (2004) Shape localization based on statistical method using extended local binary pattern. In: Proc. of the international conference on image and graphics, pp 184–187

  39. Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5), 1107–1118 (2009)

    Article  MathSciNet  Google Scholar 

  40. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit 42(3), 425–436 (2009)

    Article  MATH  Google Scholar 

  41. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  43. Yusof R, Rosli NR, Khalid M (2010) Using gabor filters as image multiplier for tropical wood species recognition system. In: 12th International conference on computer modelling and simulation, pp 284–289

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Acknowledgments

This research has been supported by The National Council for Scientific and Technological Development (CNPq) grant 301653/2011-9.

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Authors and Affiliations

  1. Federal University of Parana (UFPR), R. Rua Cel. Francisco H. dos Santos, 100, Curitiba, PR, Brazil

    Pedro L. Paula Filho, Luiz S. Oliveira & Silvana Nisgoski

  2. State University of Ponta Grossa (UEPG), Av. General Carlos Cavalcanti, 4748, Ponta Grossa, PR, Brazil

    Alceu S. Britto Jr.

  3. Pontifical Catholic University of Parana (PUCPR), Rua Imaculada Conceição, Curitiba, PR, Brazil

    Alceu S. Britto Jr.

Authors
  1. Pedro L. Paula Filho
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  2. Luiz S. Oliveira
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  3. Silvana Nisgoski
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  4. Alceu S. Britto Jr.
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Correspondence to Alceu S. Britto Jr..

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Filho, P.L.P., Oliveira, L.S., Nisgoski, S. et al. Forest species recognition using macroscopic images. Machine Vision and Applications 25, 1019–1031 (2014). https://doi.org/10.1007/s00138-014-0592-7

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