Shearlet dönüşüm ve yeni geometrik özellikler kullanılarak aşırı öğrenme makinesine dayalı bitki tanıma sistemi

Yıl 2019, , 2097 - 2112, 25.06.2019

Muammer Türkoğlu , Davut Hanbay

https://doi.org/10.17341/gazimmfd.423674

Cited By: 3

Öz

Bitki türlerini doğru tespit
edebilmek için bugüne kadar yapılan çalışmalarda farklı yaklaşımlar
kullanılmıştır. Bu yaklaşımlardan en temel olan bitki yaprakları, şekil, renk
ve damar dokusu gibi avantajlara sahip birçok özellik içermektedir. Bu
çalışmada açıdan bağımsız olarak yaprağın geometrik özelliklerine dayalı yeni
bir yaklaşım önerilmiştir. Kenar Adım (KA) olarak adlandırılan bu yöntem,
şeklin sınır eğrilerindeki kenar noktalar kullanılarak açı, merkez-kenar
uzunluğu ve kenar mesafesi gibi özelliklerden oluşmaktadır. Ayrıca doku
tanımada iyi hassasiyet göstermesi, hızlı hesaplama yapması ve yön bağımsızlığı
gibi özelliklere sahip olan Shearlet Dönüşüm yöntemi kullanılmıştır. Bu
yöntemlere ek olarak renk özellikleri ile Gri Seviye Eş-Oluşum Matrisleri
(GSEM) yöntemi uygulanmıştır. Tüm bu yöntemlerden elde edilen öznitelikler ayrı
ayrı ve bileşik olarak Aşırı Öğrenme Makineleri (AÖM) sınıflandırıcı yöntemi
ile test edilmiştir. Flavia, Swedish, ICL ve Foliage gibi dört farklı bitki
yaprak veri setleri kullanılarak önerilen çalışma test edilmiştir. Bu veri
setleri kullanılarak doku, şekil ve renk özelliklerine dayalı yapılan
çalışmalar ile önerilen yaklaşımın performansı kıyaslanmıştır. Sonuç olarak,
önerilen çalışmanın diğer çalışmalara göre daha başarılı olduğu tespit
edilmiştir.

Anahtar Kelimeler

Yaprak Tanıma, Görüntü İşleme, Shearlet Dönüşüm, Kenar Adım Yöntemi, Aşırı Öğrenme Makinesi

Kaynakça

• 1. Zhang, H., TAO, X., Leaf image recognition based on wavelet and fractal dimension, Journal of Computional System 11.1, 141-148, 2015.
• 2. Kulkarni, A. H., Rai, H. M., Jahagirdar, K. A., Upparamani, P. S., A leaf recognition technique for plant classification using RBPNN and Zernike moments, International Journal of Advanced Research in Computer and Communication Engineering 2.1, 984-988, 2013.
• 3. Shabanzade, M., Zahedi, M., Aghvami, S. A., Combination of local descriptors and global features for leaf recognition, Signal & Image Processing, 2.3: 23, 2011.
• 4. 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, Signal Processing and Information Technology, 2007 IEEE International Symposium on. IEEE, 2007.
• 5. Söderkvist, O., Computer vision classification of leaves from swedish trees, 2001.
• 6. Silva, P. F., Marcal, A. R., da Silva, R. M. A., Evaluation of features for leaf discrimination, International Conference Image Analysis and Recognition. Springer, Berlin, Heidelberg, 2013.
• 7. Kadir, A., Nugroho, L. E., Susanto, A., Santosa, P. I., Neural network application on foliage plant identification, arXiv preprint arXiv:1311.5829, 2013.
• 8. Mahdikhanlou, K., Ebrahimnezhad, H., Plant leaf classification using centroid distance and axis of least inertia method, In Electrical Engineering (ICEE), 2014 22nd Iranian Conference on. IEEE, 1690-1694, 2014.
• 9. Yasar, A., Saritas, I., Sahman, M. A., Dundar, A. O., Classification of Leaf Type Using Artificial Neural Networks, International Journal of Intelligent Systems and Applications in Engineering 3.4, 136-139, 2015.
• 10. Lee, K. B., Hong, K. S., An implementation of leaf recognition system using leaf vein and shape, International Journal of Bio-Science and Bio-Technology, 5.2: 57-66, 2013.
• 11. Kadir, A., Nugroho, L. E., Susanto, A., Santosa, P. I., Experiments of Zernike moments for leaf identification, Journal of Theoretical and Applied Information Technology (JATIT) 41.1: 82-93, 2012.
• 12. C. Sari, C. B. Akgul, B. Sankur, Combination of gross shape features, fourier descriptors and multiscale distance matrix for leaf recognition, In AÖMAR, 2013 55th International Symposium. IEEE, 23-26, 2013.
• 13. Naresh, Y. G., Nagendraswamy, H. S., Classification of medicinal plants: an approach using modified LBP with symbolic representation, Neurocomputing, 173: 1789-1797, 2016.
• 14. Kadir, A., Nugroho, L. E., Susanto, A., Santosa, P. I., Foliage plant retrieval using polar fourier Dönüşüm, color moments and vein features, arXiv preprint arXiv:1110.1513, 2011.
• 15. Elhariri, E., El-Bendary, N., Hassanien, A. E., Plant classification system based on leaf features, Computer engineering & systems (icces), 2014 9th international conference on. IEEE, 2014.
• 16. Priya, C. A., Balasaravanan, T., Thanamani, A. S., An efficient leaf recognition algorithm for plant classification using support vector machine, In Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on. IEEE, p. 428-432, 2012.
• 17. Tsolakidis, D. G., Kosmopoulos, D. I., Papadourakis, G., Plant leaf recognition using Zernike moments and histogram of oriented gradients, Hellenic Conference on Artificial Intelligence. Springer, Cham, 2014.
• 18. Kadir, A., Nugroho, L. E., Susanto, A., Santosa, P. I., Performance improvement of leaf identification system using principal component analysis, International Journal of Advanced Science and Technology 44, 113-124, 2012.
• 19. Wang, X., Liang, J., Guo, F., Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition, Digital Signal Processing 34, 101-107, 2014.
• 20. Hong, A., Chi, Z., Chen, G., Wang, Z., Region-of-Interest based flower images retrieval, Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). 2003 IEEE International Conference on. Vol. 3. IEEE, 2003.
• 21. Çalışkan, A., Acar, E., Kaya, Y., GSEM Tabanlı KNN Sınıflandırıcı Modeli İle Avuç İçi Tanıma Sistemi. Batman University, Journal of Life Sciences Volume 1, Number 2, 2012.
• 22. Hudec, R., Benco, M., Novel method for color textures features extraction based on GSEM, Radioengineering, 2007.
• 23. Wang, X., Georganas, N. D., GLCM texture based fractal method for evaluating fabric surface roughness, Electrical and Computer Engineering, 2009. CCECE'09. Canadian Conference on. IEEE, 2009.
• 24. Haralick, R. M., Shanmugam, K., Textural features for image classification." IEEE Transactions on systems, man, and cybernetics 6, 610-621, 1973.
• 25. Easley, G., Labate, D., Lim, W. Q., Sparse directional image representations using the discrete shearlet transform, Applied and Computational Harmonic Analysis, 25(1), 25-46, 2008.
• 26. Guo, K., Labate, D., Optimally sparse multidimensional representation using shearlets, SIAM journal on mathematical analysis, 39(1), 298-318, 2007.
• 27. Yaşar, H., and Ceylan, M., Investigation of image representation and denoising performances of real and complex valued fast finite shearlet transform, Signal Processing and Communications Applications Conference (SIU), 2015 23th. IEEE, 2015.
• 28. Hanbay, K., Yuvarlak örgü makineleri için görüntü işleme tabanlı kumaş hatası tespit sistemi, Inönü Üniversitesi, 2016.
• 29. Guo, K., Gitta, K. Demetrio, L., Sparse multidimensional representations using anisotropic dilation and shear operators, International Conference on the Interaction between Wavelets and Splines, 189-201, 2005.
• 30. Hauser, S. Steidl, G., Fast finite shearlet transform: a tutorial, preprint, Access: http://arxiv.org/pdf/1202.1773.pdf, 2015.
• 31. Belhumuer, P.N., Hespanha, J.P. Kriegman, D.J., Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Cilt No. 19, 711-720, 1997.
• 32. Chatterjee, C., Roychowdhury, V. P., Chong, E. K., On relative convergence properties of principal component analysis algorithms, IEEE Transactions on Neural Networks, 9(2), 319-329, 1998.
• 33. Yildiz, E., Sevim, Y., Comparison of linear dimensionality reduction methods on classification methods, Electrical, Electronics and Biomedical Engineering (ELECO), 2016 National Conference on. IEEE, 2016.
• 34. Karabatak, M., Ince, M. C., Avci, E., An expert system for diagnosis breast cancer based on Principal Component Analysis method, Signal Processing, Communication and Applications Conference, 2008. SIU 2008. IEEE 16th. IEEE, 2008.
• 35. Huang, G. B., Zhu, Q. Y. Siew, C. K., Extreme learning machine: theory and applications, Neurocomputing 70.1, 489-501, 2006.
• 36. Alçin, Ö. F., Şengür, A., İnce, M. C., İleri-geri takip algoritmasi tabanli seyrek aşiri öğrenme makinesi, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 30.1, 2015.
• 37. Huang, G. B., Zhu, Q. Y., Siew, C. K., Extreme learning machine: a new learning scheme of feedforward neural networks, Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on. Vol. 2. IEEE, 2004.
• 38. Luo, M., and Zhang, K., A hybrid approach combining extreme learning machine and sparse representation for image classification, Engineering Applications of Artificial Intelligence, Cilt 27, 228-235, 2014.
• 39. Alcin, O. F., Sengur, A., Qian, J. Ince, M. C., OMP-AÖM: orthogonal matching pursuit-based extreme learning machine for regression, Journal of Intelligent Systems 24.1, 135-143, 2015.
• 40. Alcin, O. F., Sengur, A., Ghofrani, S. Ince, M. C., GA-SAÖM: Greedy algorithms for sparse extreme learning machine, Measurement 55, 126-132, 2014.
• 41. Jain, A., Nandakumar, K., Ross, A., Score normalization in multimodal biometric systems, Pattern recognition, 38.12: 2270-2285, 2005.
• 42. Larose, D.T., Discovering Knowledge in Data: An Introduction to Data Mining, New Jersey: John Wiley and Sons Inc., 2014.
• 43. Laga, H., Kurtek, S., Srivastava, A., Miklavcic, S. J., Landmark-free statistical analysis of the shape of plant leaves, Journal of theoretical biology 363, 41-52, 2014.
• 44. Ling, H., Jacobs, D.W., Shape classification using the inner-distance, IEEE Trans. Pattern Anal. Mach. Intell. 29, 286–299, 2007.
• 45. Daliri, M.R., Torre, V., Robust symbolic representation for shape recognition and retrieval, Pattern Recogn. 41, 1782–1798, 2008.
• 46. Alajlan, N., El Rube, I., Kamel, M. S., Freeman, G., Shape retrieval using triangle-area representation and dynamic space warping, Pattern recognition, 40(7), 1911-1920, 2007.
• 47. Yang, C., Wei, H., and Yu, Q., Multiscale Triangular Centroid Distance for Shape-Based Plant Leaf Recognition, ECAI. 2016.
• 48. Mouine, S., Yahiaoui, I., Verroust-Blondet, A., A shape-based approach for leaf classification using multiscale triangular representation, in: Proceedings of the 3rd ACM International Conference on International Conference on Multimedia Retrieval, pp. 127–134, 2013.
• 49. Wang, B., Brown, D., Gao, Y., La Salle, J., MARCH: Multiscale-arch-height description for mobile retrieval of leaf images, Information Sciences 302, 132-148, 2015.
• 50. Huang, C., Han, T. X., He, Z., Multi-scale embedded descriptor for shape classification." Journal of Visual Communication and Image Representation 25.7, 1640-1646, 2014.
• 51. Qi, X., Xiao, R., Li, C. G., Qiao, Y., Guo, J., Tang, X., Pairwise rotation invariant co-occurrence local binary pattern, IEEE transactions on pattern analysis and machine intelligence 36.11, 2199-2213, 2014.