Multi-kernel dictionary learning for classifying maize varieties
Keywords:
multi-kernel, sparse representation, dictionary learning, maize classificationAbstract
The automatic classification and identification of maize varieties is one of the important research contents in agriculture. A multi-kernel maize varieties classification approach was proposed in this paper in order to improve the recognition rate of maize varieties. In this approach, four kinds of maize varieties were selected, in each variety 200 grains were selected randomly as the samples, and in each sample 160 grains were taken as the training samples randomly; the characteristics of maize grain were extracted as the typical characteristics to distinguish maize varieties, by which the dictionary required by K-SVD was constructed; for the test samples, the feature-matrixes were extracted by dimension reduction method which were mapped to the high-dimension space by muti-kernel function mapping. The high-dimension characteristic matrixes were trained by K-SVD method and the corresponding feature dictionary was obtained respectively. Finally, the test samples representing were trained and classified by l2,1 minimization sparse coefficient. The experiment results showed that recognition rate was improved obviously through this approach, and the poor-effect to maize variety identification from partial occlusion can be eliminated effectively. Keywords: multi-kernel, sparse representation, dictionary learning, maize classification DOI: 10.25165/j.ijabe.20181103.3091 Citation: Zhu H, Yue J, Li Z B, Zhang Z W. Multi-kernel dictionary learning for classifying maize varieties. Int J Agric & Biol Eng, 2018; 11(3): 183–189.References
[1] Liu C C, Shaw J-T, Poong K-Y. Classifying paddy rice by morphological and color features using machine vision. Cereal Chemistry, 2005; 82(6): 649–653.
[2] Jinorose M, Prachayawarakorn S, Soponronnarit S, Devahastinet S. Development of a computer vision system and novel evaluation criteria to characterize color and appearance of rice. Drying Technology, 2010; 28(9): 1118–1124.
[3] Yang W, Winter P, Sokhansanj S, Wood H, Crerer B. Discrimination of Hard-to-pop Popcorn Kernels by Machine Vision an d Neural Networks. Biosystems Engineering, 2005; 91(1): 1–8.
[4] Kurtulmuş F, Ali̇Baş I, Kavdi̇R I. Classification of pepper seeds using machine vision based on neural network. Int J Agric & Biol Eng, 2016; 9(1): 51–62.
[5] Guo D, Zhu Q, Huang M, Guo Y, Qin J. Model updating for the classification of different varieties of maize seeds from different years by hyperspectral imaging coupled with a pre-labeling method. Computers & Electronics in Agriculture, 2017; 142: 1–8.
[6] Zheng Y, Zhu Q, Huang M, Guo Y, Qin J. Maize and weed classification using color indices with support vector data description in outdoor fields. Computers & Electronics in Agriculture, 2017; 141: 215–222.
[7] Ambrose A, Kandpal L M, Kim M S, Lee W H, Cho B K. High speed measurement of corn seed viability using hyperspectral imaging. Infrared Physics & Technology, 2016, 75: 173–179.
[8] Vithu P, Moses J A. Machine Vision System for Food Grain Quality Evaluation: A Review. Trends in Food Science & Technology, 2016, 56: 13–20.
[9] Xie C, He Y. Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging. International Journal of Agricultural & Biological Engineering, 2018, 11(1): 187–191.
[10] Hao S, Wang W, Yan Y, Bruzzone L. Class-wise dictionary learning for hyperspectral image classification. Neurocomputing, 2016, 220.
[11] Zhang Y, Xu T, Ma J. Image Categorization using Non-negative Kernel Sparse Representation. Neurocomputing, 2017.
[12] Yang S Y, Han Y, Zhang X R. A sparse kernel representation method for image classification. International Joint Conference on Neural Networks. IEEE, 2012; 1–7.
[13] Zhang L, Zhou W D, Chang P C, Liu J, Yan Z, Wang T. Kernel Sparse Representation Based Classifier. IEEE Trans on Signal Processing, 2012, 60(4): 1684–1695.
[14] Li H, Gao Y, Sun J. Fast Kernel Sparse Representation. International Conference on Digital Image Computing Techniques and Applications. IEEE, 2011: 72–77.
[15] Meng J, Jung C. Class-discriminative kernel sparse representation-based classification using multi-objective optimization. IEEE Transactions on Signal Processing, 2013; 61(18): 4416–4427.
[16] Gao S, Tsang I W, Chia L T. Sparse representation with kernels. IEEE Transactions on Image Processing. Publication of the IEEE Signal Processing Society, 2013; 22(2): 423–434.
[17] Nguyen H V, Patel V M, Nasrabadi N M, Chellappa R. Kernel dictionary learning. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012; pp.2021–2024.
[18] Van N H, Patel V M, Nasrabadi N M, Chellappa R. Design of non-linear kernel dictionaries for object recognition. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2013; 22(12): 5123–5135.
[19] Golts A, Elad M. Linearized kernel dictionary learning. IEEE Journal of Selected Topics in Signal Processing, 2015; 10(4): 726–739.
[20] Zare T, Sadeghi M T. A Novel multiple kernel-based dictionary learning for distributive and collective sparse representation based classifiers. Neurocomputing, 2017; 234: 164–173.
[21] Xu Z, Jin R, King I, Lyu M R. An extended level method for efficient multiple kernel learning. In Advances in Neural Information Processing Systems, 2009; pp.1825–1832.
[22] Rakotomamonjy A, Bach F R, Canu S, Grandvalet Y. Simplemkl. Journal of Machine Learning Research, 2008; 9(3): 2491–2521.
[23] Xu Z, Jin R, Yang H, Lyu M R. Simple and efficient multiple kernel learning by group lasso. International Conference on Machine Learning. DBLP, 2010; pp.1175–1182.
[24] Wen X Z, Fang W, Zheng Y H. An algorithm based on Haar-like features and improved AdaBoost classifier for vehicle recognition. Acta Electronica Sinica, 2011; 39(5): 1121–1126.
[25] Kim S J, Koh K, Lustig M. An interior-point method for large-scale L2,1-regularized least squares. IEEE Journal on Selected Topics in Signal Processing, 2007; 1(4): 606–617.
[26] Bosch A, Zisserman A, Munoz X. Representing shape with a spatial pyramid kernel. ACM International Conference on Image and Video Retrieval. ACM, 2007: 401–408.
[27] Yang J, Tian Y, Duan L Y, Huang T, Gao W. Group-sensitive multiple kernel learning for object recognition. IEEE Transactions on Image Processing, 2012; 21(5): 2838–2852
[28] Feng L J, Li X J, Wen C L. Wheat varieties identification research based on sparse representation. Journal of Jiangnan University: Natural Science Edition, 2015; 14 (6): 730–735.
[29] Yang S Q, Ning J F, He D J. Identification of varieties of rice based on sparse representation. Transactions of the CSAE, 2011; 27(3): 191–195.
[2] Jinorose M, Prachayawarakorn S, Soponronnarit S, Devahastinet S. Development of a computer vision system and novel evaluation criteria to characterize color and appearance of rice. Drying Technology, 2010; 28(9): 1118–1124.
[3] Yang W, Winter P, Sokhansanj S, Wood H, Crerer B. Discrimination of Hard-to-pop Popcorn Kernels by Machine Vision an d Neural Networks. Biosystems Engineering, 2005; 91(1): 1–8.
[4] Kurtulmuş F, Ali̇Baş I, Kavdi̇R I. Classification of pepper seeds using machine vision based on neural network. Int J Agric & Biol Eng, 2016; 9(1): 51–62.
[5] Guo D, Zhu Q, Huang M, Guo Y, Qin J. Model updating for the classification of different varieties of maize seeds from different years by hyperspectral imaging coupled with a pre-labeling method. Computers & Electronics in Agriculture, 2017; 142: 1–8.
[6] Zheng Y, Zhu Q, Huang M, Guo Y, Qin J. Maize and weed classification using color indices with support vector data description in outdoor fields. Computers & Electronics in Agriculture, 2017; 141: 215–222.
[7] Ambrose A, Kandpal L M, Kim M S, Lee W H, Cho B K. High speed measurement of corn seed viability using hyperspectral imaging. Infrared Physics & Technology, 2016, 75: 173–179.
[8] Vithu P, Moses J A. Machine Vision System for Food Grain Quality Evaluation: A Review. Trends in Food Science & Technology, 2016, 56: 13–20.
[9] Xie C, He Y. Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging. International Journal of Agricultural & Biological Engineering, 2018, 11(1): 187–191.
[10] Hao S, Wang W, Yan Y, Bruzzone L. Class-wise dictionary learning for hyperspectral image classification. Neurocomputing, 2016, 220.
[11] Zhang Y, Xu T, Ma J. Image Categorization using Non-negative Kernel Sparse Representation. Neurocomputing, 2017.
[12] Yang S Y, Han Y, Zhang X R. A sparse kernel representation method for image classification. International Joint Conference on Neural Networks. IEEE, 2012; 1–7.
[13] Zhang L, Zhou W D, Chang P C, Liu J, Yan Z, Wang T. Kernel Sparse Representation Based Classifier. IEEE Trans on Signal Processing, 2012, 60(4): 1684–1695.
[14] Li H, Gao Y, Sun J. Fast Kernel Sparse Representation. International Conference on Digital Image Computing Techniques and Applications. IEEE, 2011: 72–77.
[15] Meng J, Jung C. Class-discriminative kernel sparse representation-based classification using multi-objective optimization. IEEE Transactions on Signal Processing, 2013; 61(18): 4416–4427.
[16] Gao S, Tsang I W, Chia L T. Sparse representation with kernels. IEEE Transactions on Image Processing. Publication of the IEEE Signal Processing Society, 2013; 22(2): 423–434.
[17] Nguyen H V, Patel V M, Nasrabadi N M, Chellappa R. Kernel dictionary learning. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012; pp.2021–2024.
[18] Van N H, Patel V M, Nasrabadi N M, Chellappa R. Design of non-linear kernel dictionaries for object recognition. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2013; 22(12): 5123–5135.
[19] Golts A, Elad M. Linearized kernel dictionary learning. IEEE Journal of Selected Topics in Signal Processing, 2015; 10(4): 726–739.
[20] Zare T, Sadeghi M T. A Novel multiple kernel-based dictionary learning for distributive and collective sparse representation based classifiers. Neurocomputing, 2017; 234: 164–173.
[21] Xu Z, Jin R, King I, Lyu M R. An extended level method for efficient multiple kernel learning. In Advances in Neural Information Processing Systems, 2009; pp.1825–1832.
[22] Rakotomamonjy A, Bach F R, Canu S, Grandvalet Y. Simplemkl. Journal of Machine Learning Research, 2008; 9(3): 2491–2521.
[23] Xu Z, Jin R, Yang H, Lyu M R. Simple and efficient multiple kernel learning by group lasso. International Conference on Machine Learning. DBLP, 2010; pp.1175–1182.
[24] Wen X Z, Fang W, Zheng Y H. An algorithm based on Haar-like features and improved AdaBoost classifier for vehicle recognition. Acta Electronica Sinica, 2011; 39(5): 1121–1126.
[25] Kim S J, Koh K, Lustig M. An interior-point method for large-scale L2,1-regularized least squares. IEEE Journal on Selected Topics in Signal Processing, 2007; 1(4): 606–617.
[26] Bosch A, Zisserman A, Munoz X. Representing shape with a spatial pyramid kernel. ACM International Conference on Image and Video Retrieval. ACM, 2007: 401–408.
[27] Yang J, Tian Y, Duan L Y, Huang T, Gao W. Group-sensitive multiple kernel learning for object recognition. IEEE Transactions on Image Processing, 2012; 21(5): 2838–2852
[28] Feng L J, Li X J, Wen C L. Wheat varieties identification research based on sparse representation. Journal of Jiangnan University: Natural Science Edition, 2015; 14 (6): 730–735.
[29] Yang S Q, Ning J F, He D J. Identification of varieties of rice based on sparse representation. Transactions of the CSAE, 2011; 27(3): 191–195.
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Published
2018-06-01
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Zhu, H., Yue, J., Li, Z., & Zhang, Z. (2018). Multi-kernel dictionary learning for classifying maize varieties. International Journal of Agricultural and Biological Engineering, 11(3), 183–189. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3091
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Information Technology, Sensors and Control Systems
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