Multi-kernel dictionary learning for classifying maize varieties
Abstract
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.
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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.
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.
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.
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.
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.
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.
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.
Vithu P, Moses J A. Machine Vision System for Food Grain Quality Evaluation: A Review. Trends in Food Science & Technology, 2016, 56: 13–20.
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.
Hao S, Wang W, Yan Y, Bruzzone L. Class-wise dictionary learning for hyperspectral image classification. Neurocomputing, 2016, 220.
Zhang Y, Xu T, Ma J. Image Categorization using Non-negative Kernel Sparse Representation. Neurocomputing, 2017.
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.
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.
Li H, Gao Y, Sun J. Fast Kernel Sparse Representation. International Conference on Digital Image Computing Techniques and Applications. IEEE, 2011: 72–77.
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.
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.
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.
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.
Golts A, Elad M. Linearized kernel dictionary learning. IEEE Journal of Selected Topics in Signal Processing, 2015; 10(4): 726–739.
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.
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.
Rakotomamonjy A, Bach F R, Canu S, Grandvalet Y. Simplemkl. Journal of Machine Learning Research, 2008; 9(3): 2491–2521.
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.
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.
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.
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.
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
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.
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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