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Learning-Based Leaf Image Recognition Frameworks

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Intelligent Systems in Science and Information 2014 (SAI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 591))

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Abstract

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this chapter, we propose two learning-based leaf image recognition frameworks for automatic plant identification and conduct a comparative study between them with existing approaches. First, we propose to learn sparse representation for leaf image recognition. In order to model leaf images, we learn an over-complete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Second, we also propose a general bag-of-words (BoW) model-based recognition system for leaf images, mainly used for comparison. We experimentally compare the two learning-based approaches and show unique characteristics of our sparse representation-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two proposed methods. We also show that the proposed sparse representation-based framework can outperform our BoW-based one and state-of-the-art approaches, conducted on the same dataset.

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References

  1. Mzoughi, O., Yahiaoui, I., Boujemaa, N., Zagrouba, E.: Advanced tree species identification using multiple leaf parts image queries. In: Proceedings of IEEE International Conference on Image Processing, pp. 3967–3971, Melbourne, Sept 2013

    Google Scholar 

  2. Mouine, O., Yahiaoui, I., Verroust-Blondet, A.: Advanced shape context for plant species identification using leaf image retrieval. In: Proceedings of ACM International Conference on Multimedia Retrieval, June 2012

    Google Scholar 

  3. Mzoughi, O., Yahiaoui, I., Boujemaa, N.: Petiole shape detection for advanced leaf identification. In: Proceedings of IEEE International Conference on Image Processing, pp. 1033–1036, Orlando, FL, USA, Sept 2012

    Google Scholar 

  4. Yahiaoui, I., Mzoughi, O., Boujemaa, N.: Leaf shape descriptor for tree species identification. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 254–259, Melbourne, July 2012

    Google Scholar 

  5. Mouine, S., Yahiaoui, I., Verroust-Blondet, A.: A shape-based approach for leaf classification using multiscale triangular representation. In: Proceedings of ACM International Conference on Multimedia Retrieval, pp. 127–134, Dallas, Texas, USA, Apr 2013

    Google Scholar 

  6. Caballero, C., Aranda, M.C.: Plant species identification using leaf image retrieval. In: Proceedings of ACM International Conference on Image and Video Retrieval, pp. 327–334, July 2010

    Google Scholar 

  7. Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B.: Leafsnap: a computer vision system for automatic plant species identification. In: Proceedings of European Conference on Computer Vision, pp. 502–516, Florence, Italy, Oct 2012

    Google Scholar 

  8. Mouine, S., Yahiaoui, I., Verroust-Blondet, A., Joyeux, L., Selmi, S., Goëau, H.: An android application for leaf-based plant identification. In: Proceedings of ACM International Conference on Multimedia Retrieval, Dallas, Texas, USA, Apr 2013

    Google Scholar 

  9. Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.-X., Chang, Y.-F., Xiang, Q.-L.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: Proceedings of IEEE International Symposium on Signal Processing and Information Technology, pp. 11–16, Giza, Egypt (the leaf image dataset available from http://sourceforge.net/projects/flavia/files/), Dec 2007

  10. Hossain, J., Amin, M.A.: Leaf shape identification based plant biometrics. In: Proceedings of IEEE International Conference on Computer and Information Technology, pp. 458–463, Dhaka, Dec 2010

    Google Scholar 

  11. Du, J.-X., Wang, X.-F., Zhang, G.-J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007)

    Article  MATH  Google Scholar 

  12. Wang, X., Huang, D.-S., Du, J.-X., Xu, H., Heutte, L.: Classification of plant leaf images with complicated background. Appl. Math. Comput. 205(2), 916–926 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  13. Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: Leaf classification using shape, color, and texture features. Int. J. Comput. Trends Technol. 1(3), 225–230 (2011)

    Google Scholar 

  14. Sari, C., Akgul, C.B., Sankur, B.: Combination of gross shape features, fourier descriptors and multiscale distance matrix for leaf recognition. In: Proceedings of International Symposium on ELMAR, pp. 23–26, Zadar, Croatia, Sept 2013

    Google Scholar 

  15. Du, J.-X., Zhai, C.-M., Wang, Q.-P.: Recognition of plant leaf image based on fractal dimension features. Neurocomputing 116, 150–156 (2013)

    Article  Google Scholar 

  16. Wang, Z., Chi, Z., Feng, D.: Shape based leaf image retrieval. IEEE Proc. Vis. Image Sig. Process. 150(1), 34–43 (2003)

    Article  Google Scholar 

  17. Kebapci, H., Yanikoglu, B., Unal, G.: Plant image retrieval using color, shape and texture features. Comput. J. 54(9), 1475–1490 (2011)

    Article  Google Scholar 

  18. Fotopoulou, F., Laskaris, N., Economou, G., Fotopoulos, S.: Advanced leaf image retrieval via multidimensional embedding sequence similarity (MESS) method. Pattern Anal. Appl. 16(3), 381–392 (2013)

    Article  MathSciNet  Google Scholar 

  19. Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)

    Article  Google Scholar 

  20. Guha, T., Ward, R.K.: Learning sparse representations for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1576–1588 (2012)

    Article  Google Scholar 

  21. Huang, D.-A., Kang, L.-W., Wang, Y.-C.F., Lin, C.-W.: Self-learning based image decomposition with applications to single image denoising. IEEE Trans. Multimedia 16(1), 83–93 (2014)

    Article  Google Scholar 

  22. Chen, D.-Y., Chen, C.-C., Kang, L.-W.: Visual depth guided color image rain streaks removal using sparse coding. IEEE Trans. Circuits Syst. Video Technol. 24(8), 1430–1455 (2014)

    Article  Google Scholar 

  23. Kang, L.-W., Lin, C.-W., Fu, Y.-H.: Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans. Image Process. 21(4), 1742–1755 (2012)

    Article  MathSciNet  Google Scholar 

  24. Yeh, C.-H., Kang, L.-W., Chiou, Y.-W., Lin, C.-W., Fan Jiang, S.-J.: Self-learning-based post-processing for image/video deblocking via sparse representation. J. Vis. Comm. Image Rep. 25(5), 891–903 (2014)

    Article  Google Scholar 

  25. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res 11, 19–60 (2010)

    MATH  MathSciNet  Google Scholar 

  26. Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (Matlab source code available from http://www.cs.technion.ac.il/~ronrubin/software.html) (2006)

  27. Bruckstein, A.M., Donoho, D.L., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51(1), 34–81 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  28. Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(13), 607–609 (1996)

    Article  Google Scholar 

  29. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proceedings of ECCV International Workshop on Statistical Learning in Computer Vision, Prague (2004)

    Google Scholar 

  30. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 2169–2178 (2006)

    Google Scholar 

  31. Yang, J., Jiang, Y.-G., Hauptmann, A.G., Ngo, C.-W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of ACM Multimedia Information Retrieval, pp. 197–206 (2007)

    Google Scholar 

  32. Hsu, C.-Y., Kang, L.-W., Liao, H.-Y.M.: Cross-camera vehicle tracking via affine invariant object matching for video forensics applications. In: Proceedings of IEEE International Conference on Multimedia and Expo, San Jose, CA, USA, July 2013

    Google Scholar 

  33. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), article no. 27 (Matlab interface source code available from http://www.csie.ntu.edu.tw/~cjlin/libsvm/), Apr 2011

  34. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis 60(2), 91–110 (2004)

    Article  Google Scholar 

  35. Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)

    Article  MATH  Google Scholar 

  36. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 3360–3367, San Francisco, CA, USA, June 2010

    Google Scholar 

  37. Tsai, C.-Y., Huang, D.-A., Yang, M.-C., Kang, L.-W., Wang, Y.-C.F.: Context-aware single image super-resolution using locality-constrained group sparse representation. In: Proceedings of IEEE Visual Communications and Image Processing Conference, San Diego, CA, USA, Nov 2012

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Jou-Ken Hsiao, Li-Wei Kang & Ching-Long Chang

  2. Graduate School of Engineering Science and Technology-Doctoral Program, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Li-Wei Kang

  3. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Chih-Yang Lin

Authors
  1. Jou-Ken Hsiao
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  2. Li-Wei Kang
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  3. Ching-Long Chang
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  4. Chih-Yang Lin
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Corresponding author

Correspondence to Li-Wei Kang .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, London, United Kingdom

    Rahul Bhatia

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Hsiao, JK., Kang, LW., Chang, CL., Lin, CY. (2015). Learning-Based Leaf Image Recognition Frameworks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-14654-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14653-9

  • Online ISBN: 978-3-319-14654-6

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