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Implementation of Leaf Image Recognition System Based on LBP and B/S Framework

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

Plant identification system is on the basis of the previous, through continuous optimizing all aspects of the algorithm to improve efficiency and accuracy of the algorithm. For feature extraction, since the local binary pattern was proposed in the past decades, it has been widely used in computer vision to describe the feature for image classification such as image recognition, motion detection and medical image analysis. According to accuracy of the descriptor always fluctuates with different samples, some improved pattern of LBP has been presented in papers. Complete Local Binary Pattern (CLBP) is an optimized version which set an additional magnitude value to local differences. This paper shows extensive experiments of implement the LBP derivatives for plants texture identification. Finally realize an online system to identify what kind of the plant image user uploaded based on LBP descriptor.

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References

  1. Ojala, T., Pietikainen, M., Maenpaa, 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 

  2. Zhou, H., Wang, R., Wang, C.: A novel extended local binary pattern operator for texture analysis. Inf. Sci. 178(22), 4314–4325 (2008)

    Article  MATH  Google Scholar 

  3. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  4. Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: ICCV (2009)

    Google Scholar 

  5. Zhao, Y., Huang, D.-S., Jia, W.: Completed local binary count for rotation invariant texture. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  7. Tan, X., Triggs, B.: Fusing gabor and LBP feature sets for kernel-based face recognition. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Face and Gesture, pp. 235–249 (2007)

    Google Scholar 

  8. Wang, J.-G., Yau, W.-Y., Wang, H. L.: Age categorization via ECOC with fused gabor and LBP features. In: Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV), pp. 313–318 (2009)

    Google Scholar 

  9. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6), 610–621 (1973)

    Article  Google Scholar 

  10. http://www.intelengine.cn/English/dataset

  11. Bremner, D., Demainem, E., Erickson, J., Iacono, J., Langerman, S., Morin, P., Toussaint, G.: Output-sensitive algorithms for computing nearest-neighbor decision boundaries. Discrete Comput. Geometry 33(4), 593–604 (2005). doi:10.1007/s00454-004-1152-0

    Article  Google Scholar 

  12. Coomans, D., Massart, D.L.: Alternative k-nearest neighbour rules in supervised pattern recognition : part 1. k-nearest neighbour classification by using alternative voting rules. Anal. Chim. Acta 136, 15–27 (1982). doi:10.1016/S0003-2670(01)95359-0

    Article  Google Scholar 

  13. Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit Artif Intell. 13(7), 1083–1101 (1999)

    Article  Google Scholar 

  14. Guyer, D., Miles, G., Schreiber, M., Mitchell, O., Vanderbilt, V.: Machine vision and image processing for plant identification. Trans. ASAE 29, 1500–1507 (1986)

    Article  Google Scholar 

  15. Wang, X.-F., Huang, D.-S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43, 603–618 (2010)

    Article  MATH  Google Scholar 

  16. Huang, D.-S.: Systematic theory of neural networks for pattern recognition, vol. 28, pp. 323–332. Publishing House of Electronic Industry of China, Beijing (1996)

    Google Scholar 

  17. Yu, H.-J., Huang, D.-S.: Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids. IEEE/ACM Transact. Comput. Biol. Bioinform. (TCBB) 10, 457–467 (2013)

    Article  Google Scholar 

  18. Huang, D.-S., Jiang, W.: A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42, 1489–1500 (2012)

    Article  Google Scholar 

  19. Huang, D.-S., Du, J.-X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Networks 19, 2099–2115 (2008)

    Article  Google Scholar 

  20. Wang, X.-F., Huang, D.-S.: A novel density-based clustering framework by using level set method. IEEE Trans. Knowl. Data Eng. 21, 1515–1531 (2009)

    Article  Google Scholar 

  21. Shang, L., Huang, D.-S., Du, J.-X., Zheng, C.-H.: Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network. Neurocomputing 69, 1782–1786 (2006)

    Article  Google Scholar 

  22. Zhao, Z.-Q., Huang, D.-S., Sun, B.-Y.: Human face recognition based on multi-features using neural networks committee. Pattern Recogn. Lett. 25, 1351–1358 (2004)

    Article  Google Scholar 

  23. Huang, D., Ip, H., Chi, Z.: A neural root finder of polynomials based on root moments. Neural Comput. 16, 1721–1762 (2004)

    Article  MATH  Google Scholar 

  24. Huang, D.-S.: A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans. Neural Networks 15, 477–491 (2004)

    Article  Google Scholar 

  25. Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. Advances in neural information processing systems, pp. 231–238 (1995)

    Google Scholar 

  26. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: ICML, pp. 148–156

    Google Scholar 

  27. Li, B., Zheng, C.-H., Huang, D.-S.: Locally linear discriminant embedding: an efficient method for face recognition. Pattern Recogn. 41, 3813–3821 (2008)

    Article  MATH  Google Scholar 

  28. Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14, 2091–2106 (2005)

    Article  MathSciNet  Google Scholar 

  29. Sajedi, H., Jamzad, M.: A contourlet-based face detection method in color images. In: Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, SITIS 2007, pp. 727–732. IEEE (2007)

    Google Scholar 

  30. Boukabou, W.R., Bouridane, A.: Contourlet-based feature extraction with PCA for face recognition. In: NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2008, pp. 482–486. IEEE (2008)

    Google Scholar 

  31. Rahati, S., Moravejian, R., Mohamad, E., Mohamad, F.: Vehicle recognition using contourlet transform and SVM. In: Fifth International Conference on Information Technology: New Generations, ITNG 2008, pp. 894–898. IEEE (2008)

    Google Scholar 

  32. Xiangbin, Z.: Texture classification based on contourlet and support vector machines. In: 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, pp. 521–524

    Google Scholar 

  33. Liu, Z., Fan, X., Lv, F.: SAR image segmentation using contourlet and support vector machine. In: Fifth International Conference on Natural Computation, ICNC 2009, pp. 250–254. IEEE (2009)

    Google Scholar 

  34. Wang, J., Ge, Y.: Texture feature recognition based on Contourlet transform and support vector machine. Jisuanji Yingyong/ J. Comput. Appl. 33 (2013)

    Google Scholar 

  35. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Article  Google Scholar 

  36. Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media, New York (2000)

    Book  MATH  Google Scholar 

  37. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)

    Article  Google Scholar 

  38. Schöllkopf, B., Burges, C.J., Smola, A.J.: Advances in Kernel Methods: Support Vector Learning. MIT press, Cambridge (1999)

    Google Scholar 

  39. Soderkvist, O.J.O.: Computer Vision Classication of Leaves from Swedish Trees (2001)

    Google Scholar 

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Acknowledgments

This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61373098 and 61272333, China Postdoctoral Science Foundation Grant, Nos. 2014M561513, and partly supported by the National High-Tech R&D Program (863) (2014AA021502 & 2015AA020101), and the grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120072110040), and the grant from the Outstanding Innovative Talent Program Foundation of Henan Province, No. 134200510025.

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Tongji University, 4800 Cao’an Highway, Jiading, Shanghai, China

    Sen Zhao & Xiao-Ping Zhang

  2. Department of Communication Technology, College of Electronic Information Engineering, Suzhou Vocational University, Suzhou, 215104, Jiangsu, China

    Li Shang

  3. College of Mechanical and Electrical Engineering, Nanchang Institute of Technology, Nanchang, 330099, Jiangxi, China

    Zhi-Kai Huang

  4. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Hao-Dong Zhu & Yong Gan

Authors
  1. Sen Zhao
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  2. Xiao-Ping Zhang
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  3. Li Shang
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  4. Zhi-Kai Huang
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  5. Hao-Dong Zhu
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  6. Yong Gan
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Corresponding author

Correspondence to Sen Zhao .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Zhao, S., Zhang, XP., Shang, L., Huang, ZK., Zhu, HD., Gan, Y. (2015). Implementation of Leaf Image Recognition System Based on LBP and B/S Framework. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_66

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

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