Abstract
In recent years, leaf image recognition and classification has become one of the most important subjects in computer vision. Many approaches have been proposed to recognise and classify leaf images relying on features extraction and selection algorithms. In this paper, a concept of distinctive hybrid descriptor is proposed consisting of both global and local features. HSV Colour histogram (HSV-CH) is extracted from leaf images as the global features, whereas Local Binary Pattern after two level wavelet decomposition (WavLBP) is extracted to represent the local characteristics of leaf images. A hybrid method, namely “Hybrid Descriptor” (HD), is then proposed considering both the global and local features. The proposed method has been empirically evaluated using four data sets of leaf images with 256 \(\times \) 256 pixels. Experimental results indicate that the performance of proposed method is promising – the HD outperformed typical leaf image recognising approaches as baseline models in experiments. The presented work makes clear, significant contribution to knowledge advancement in leaf recognition and image classification.
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Ahmed, N., et al.: An automatic leaf based plant identification system. Sci. Int. 28(1), 427–430 (2016)
Casanova, D., et al.: Plant leaf identification using Gabor wavelets. Int. J. Imaging Syst. Technol. 19(3), 236–243 (2009)
Chen, W., et al.: Identifying computer graphics using HSV colour model and statistical moments of characteristic functions. In: IEEE International Conference on Multimedia and Expo, pp. 1123–1126. IEEE (2007)
Du, J.-X., et al.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007)
Du, L., et al.: Wavelet domain local binary pattern features for writer identification. In: 20th International Conference on Pattern Recognition (ICPR), pp. 3691–3694. IEEE (2010)
Ehsanirad, A., Sharath Kumar, Y.: Leaf recognition for plant classification using GLCM and PCA methods. Oriental J. Comput. Sci. Technol. 3(1), 31–36 (2010)
Ganesh, S.S., et al.: Object identification using wavelet transform. Indian J. Sci. Technol. 9(5), 1–7 (2016)
Gu, X., Du, J.-X., Wang, X.-F.: Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 253–262. Springer, Heidelberg (2005). doi:10.1007/11538059_27
Halawani, A., et al.: Fundamentals and applications of image retrieval: an overview. Datenbank-Spektrum 18(14–23), 6 (2006)
Handa, A., Agarwal, R.: A review and a comparative study of various plant recognition and classification techniques using leaf images. Int. J. Comput. Appl. 123(2), 20–25 (2015)
Hasim, A., et al.: Leaf Shape Recognition using Centroid Contour Distance. IOP Conference Series: Earth and Environmental Science, pp. 1-8. IOP Publishing (2016)
He, Y., Sang, N.: Multi-ring local binary patterns for rotation invariant texture classification. Neural Comput. Appl. 22, 793–802 (2013)
Herdiyeni, Y., Santoni, M.M.: Combination of morphological, local binary pattern variance and colour moments features for Indonesian medicinal plants identification. In: 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 255-259. IEEE (2012)
Ibrahim, A.A.: Iris recognition using Haar wavelet transform. J. Al-Nahrain Univ. Sci. 17(1), 180–186 (2014)
Im, C., et al.: Recognizing plant species by normalized leaf shapes. In: Vision Interface, pp. 397–404 (1999)
Kadir, A., et al.: Performance improvement of leaf identification system using principal component analysis. Int. J. Adv. Sci. Technol. 44, 113–124 (2012)
Kadir, A., et al.: Neural network application on foliage plant identification, 29(9), 15–22 (2013). ArXiv preprint arXiv:1311.5829
Kang, J., Zhang, W.: A framework for image retrieval with hybrid features. In: 24th Chinese Control and Decision Conference (CCDC), pp. 1326–1330. IEEE (2012)
Liu, X., et al.: Face features extraction based on multi-scale LBP. In: 2nd International Conference on Signal Processing Systems (ICSPS), pp. 438–441. IEEE (2010)
Liu, N., Kan, J.-M.: Plant leaf identification based on the multi-feature fusion and deep belief networks method. J. Beijing For. Univ. 3, 14 (2016)
Man, Q.-K., Zheng, C.-H., Wang, X.-F., Lin, F.-Y.: Recognition of plant leaves using support vector machine. In: Huang, D.-S., Wunsch II, D.C., Wang, X.F., Lin, F.-Y. (eds.) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. Communications in Computer and Information Science, vol. 15, pp. 192–199. Springer, Heidelberg (2008)
Mohamed, A.A., et al.: Avatar face recognition using wavelet transform and hierarchical multi-scale LBP. In: 10th International Conference on Machine Learning and Applications and Workshops (ICMLA). IEEE (2011)
Nirapure, D., Reddy, U.: AST retrieval of images using filtered HSV colour level detection. Int. J. Emerg. Technol. Adv. Eng. 3(8), 414–419 (2013)
Ojala, T., et al.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Pornpanomchai, C., et al.: Thai herb leaf image recognition system (THLIRS). Kasetsart J. (Nat. Sci.) 45, 551–562 (2011)
Ruchika, M.S., Singh, A.R.: Compression of medical images using wavelet transforms. Int. J. Soft Comput. Eng. (IJSCE), pp. 2231–2307 (2012)
VijayaLakshmi, B., Mohan, V.: Kernel-based PSO and FRVM: an automatic plant leaf type detection using texture, shape, and colour features. Comput. Electron. Agric. 125, 99–112 (2016)
Wang, X.-F., Du, J.-X., Zhang, G.-J.: Recognition of leaf images based on shape features using a hypersphere classifier. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 87–96. Springer, Heidelberg (2005). doi:10.1007/11538059_10
Wang, Y., et al.: LBP texture analysis based on the local adaptive Niblack algorithm. In: IEEE Congress on Image and Signal Processing (CISP 2008), pp. 777-780 (2008)
Wang, Y.-D., et al.: Hand vein recognition based on multi-scale LBP and wavelet. In: IEEE International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 214–218 (2011)
Wang, Z., et al.: Leaf recognition based on PCNN. Neural Comput. Appl. 27(4), 899–908 (2016)
Zulkifli, Z., et al.: Plant leaf identification using moment invariants & general regression neural network. In: IEEE 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 430–435 (2011)
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Al-kharaz, A.A., Tao, X., Zhang, J., Lafta, R. (2016). Adopting Hybrid Descriptors to Recognise Leaf Images for Automatic Plant Specie Identification. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_15
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