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May
2021, 4(2): 73-88.
doi: 10.3934/mfc.2021003
Semi-Supervised classification of hyperspectral images using discrete nonlocal variation Potts Model
College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China |
The classification of Hyperspectral Image (HSI) plays an important role in various fields. To achieve more precise multi-target classification in a short time, a method for combining discrete non-local theory with traditional variable fraction Potts models is presented in this paper. The nonlocal operator makes better use of the information in a certain region centered on that pixel. Meanwhile, adding the constraint in the model can ensure that every pixel in HSI has only one class. The proposed model has the characteristics of non-convex, nonlinear, and non-smooth so that it is difficult to achieve global optimization results. By introducing a series of auxiliary variables and using the alternating direction method of multipliers, the proposed classification model is transformed into a series of convex subproblems. Finally, we conducted comparison experiments with support vector machine (SVM), K-nearest neighbor (KNN), and convolutional neural network (CNN) on five different dimensional HSI data sets. The numerical results further illustrate that the proposed method is stable and efficient and our algorithm can get more accurate predictions in a shorter time, especially when classifying data sets with more spectral layers.
Keywords:
Hyperspectral images,
Alternating Direction Method of Multipliers (ADMM),
discrete nonlocal variation Potts model,
semi-supervised classification.
Mathematics Subject Classification:
Primary: 58F15, 58F17;Secondary: 53C35.
Citation:
Linyao Ge, Baoxiang Huang, Weibo Wei, Zhenkuan Pan. Semi-Supervised classification of hyperspectral images using discrete nonlocal variation Potts Model. Mathematical Foundations of Computing,
2021, 4
(2)
: 73-88.
doi: 10.3934/mfc.2021003
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|
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M. D. Farrell and R. M. Mersereau,
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W. Hu, Y. Huang, L. Wei, F. Zhang and H. Li,
Deep convolutional neural networks for hyperspectral image classification, J. Sensors, 2015 (2015), 1-12.
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K. Huang, S. Li, X. Kang and L. Fang, Spectral-spatial hyperspectral image classification based on KNN, Sensing and Imaging, 17 (2016).
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| [19] |
G. Huo, S. X. Yang, Q. Li and Y. Zhou,
A robust and fast method for sidescan sonar image segmentation using nonlocal despeckling and active contour model, IEEE Transactions on Cybernetics, 47 (2017), 855-872.
doi: 10.1109/TCYB.2016.2530786. |
| [20] |
S. Jia, L. Shen and Q. Li,
Gabor feature-based collaborative representation for hyperspectral imagery classification, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 1118-1129.
doi: 10.1109/TGRS.2014.2334608. |
| [21] |
S. Kaewpijit, J. Le Moigne and T. El-Ghazawi,
Automatic reduction of hyperspectral imagery using wavelet spectral analysis, IEEE Transactions on Geoscience and Remote Sensing, 41 (2003), 863-871.
doi: 10.1109/TGRS.2003.810712. |
| [22] |
Y. LeCun, Y. Bengio and G. Hinton,
Deep learning, Nature, 521 (2015), 436-444.
doi: 10.1038/nature14539. |
| [23] |
F. Li, M. K. Ng, T. Y. Zeng and C. Shen,
A multiphase image segmentation method based on fuzzy region competition, SIAM J. Imaging Sci., 3 (2010), 277-299.
doi: 10.1137/080736752. |
| [24] |
G. Li, C. Zhang, F. Gao and X. Zhang,
Doubleconvpool-structured 3D-CNN for hyperspectral remote sensing image classification, J. Image and Graphics, 24 (2019), 639-654.
doi: 10.11834/jig.180422. |
| [25] |
F. Melgani and L. Bruzzone,
Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42 (2004), 1778-1790.
doi: 10.1109/TGRS.2004.831865. |
| [26] |
E. Merkurjev, J. Sunu and A. L. Bertozzi, Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video, 2014 IEEE International Conference on Image Processing (ICIP), (2014), 689–693.
doi: 10.1109/ICIP. 2014.7025138. |
| [27] |
B. Merriman, J. K. Bence and S. J. Osher,
Motion of multiple junctions: A level set approach, J. Comput. Phys., 112 (1994), 334-363.
doi: 10.1006/jcph.1994.1105. |
| [28] |
D. Mumford and J. Shah,
Optimal approximation by piecewise smooth function and associated variational problems, Comm. Pure Appl. Math., 42 (1989), 577-685.
doi: 10.1002/cpa.3160420503. |
| [29] |
M. Myllykoski R. Glowinski, T. Kärkkäinen and T. Rossi,
A new augmented Lagrangian approach for $L^1$-mean curvature image denoising, SIAM J. Imaging Sci., 8 (2015), 95-125.
doi: 10.1137/140962164. |
| [30] |
R. B. Potts, Some generalized order-disorder transformations, in Mathematical Proceedings of the Cambridge Philosophical Society, 48, Cambridge Philosophical Society, 1952, 106–109.
doi: 10.1017/S0305004100027419. |
| [31] |
L. Shen and S. Jia,
Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification, IEEE Transactions on Geoscience and Remote Sensing, 49 (2011), 5039-5046.
doi: 10.1109/TGRS.2011.2157166. |
| [32] |
J. Wang and C.-I. Chang,
Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis, IEEE Transactions on Geoscience and Remote Sensing, 44 (2006), 1586-1600.
doi: 10.1109/TGRS.2005.863297. |
| [33] |
P. Wang and X. Y. Zhu, Model selection of SVM with RBF kernel and its application, Comput Engrg. Appl., (2003), 72–73. |
| [34] |
Y. Wang and L. Wang,
Local Gabor convolutional neural network for hyperspectral image classification, Comput. Sci., 47 (2020), 151-156.
doi: 10.11896/jsjkx.190500147. |
| [35] |
C. Yi, L. F. Zhang and X. Zhang, et al., Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village), J. Remote Sensing, (2019).
doi: 10.11834/jrs. 20209065. |
| [36] |
L. Zhang, H. Sun, Z. Rao and H. Ji, Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 229 (2020).
doi: 10.1016/j. saa. 2019.117973. |
| [37] |
X. Zhang, B. Zhang, L. Zhang and Y. Sun, Hyperspectral remote sensing dataset for tea farm, Global Change Data Repository, (2017).
doi: 10.3974/geodb. 2017.03.04. V1. |
| [38] |
W. Zhu, V. Chayes, A. Tiard and et al.,
Unsupervised classification in hyperspectral imagery with nonlocal total variation and primal-dual hybrid gradient algorithm, IEEE Transactions on Geoscience and Remote Sensing, 55 (2017), 2786-2798.
doi: 10.1109/TGRS.2017.2654486. |
| [39] |
S. Zhuo, X. S. Guo and J. Wan, et al., Fast classification algorithm for polynomial kernel support vector machines, Comput. Engrg., 33 (2007). |
show all references
References:
| [1] |
A. L. Bertozzi and A. Flenner,
Diffuse interface models on graphs for classification of high dimensional data, Multiscale Model. Simul., 10 (2012), 1090-1118.
doi: 10.1137/11083109X. |
| [2] |
C. Bo, H. Lu and D. Wang,
Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification, Multimedia Tools Appl., 77 (2018), 10419-10436.
doi: 10.1007/s11042-017-4403-9. |
| [3] |
B. E. Boser, I. M. Guyon and V. N. Vapnik,
A training algorithm for optimal margin classifier, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 5 (1992), 144-152.
doi: 10.1145/130385.130401. |
| [4] |
A. Buades, B. Coll and J. M. Morel,
A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 4 (2015), 490-530.
doi: 10.1137/040616024. |
| [5] |
Y. Cai, X. F. Zhu and Z. Sun,
Semi-supervised and ensemble learning: A review, Comput. Sci., 44 (2017), 7-13.
|
| [6] |
G. Camps-Valls and L. Bruzzone,
Kernel-based methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 43 (2005), 1351-1362.
doi: 10.1109/TGRS.2005.846154. |
| [7] |
G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari and et al.,
Composite kernels for hyperspectral image classification, IEEE Geoscience and Remote Sensing Letters, 3 (2006), 93-97.
doi: 10.1109/LGRS.2005.857031. |
| [8] |
C. -I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Springer, 2003.
doi: 10.1007/978-1-4419-9170-6. |
| [9] |
Z. Dou, B. Zhang and X. Yu, A new alternating minimization algorithm for image segmentation, 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN), 2015.
doi: 10.1049/cp. 2015.0936. |
| [10] |
M. D. Farrell and R. M. Mersereau,
On the impact of PCA dimension reduction for hyperspectral detection of difficult targets, Geoscience and Remote Sensing Letters, 2 (2005), 192-195.
doi: 10.1109/LGRS.2005.846011. |
| [11] |
C. Garcia-Cardona, E. Merkurjev, A. L. Bertozzi and et al.,
Multiclass data segmentation using diffuse interface methods on graphs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (2014), 1600-1613.
doi: 10.1109/TPAMI.2014.2300478. |
| [12] |
G. Gilboa and S. Osher,
Nonlocal Operators with Applications to Image Processing, Multiscale Model. Simul., 7 (2008), 1005-1028.
doi: 10.1137/070698592. |
| [13] |
T. Goldstein, B. O'Donoghue, S. Setzer and R. Baraniuk,
Fast alternating direction optimization methods, SIAM J. Imaging Sci., 7 (2014), 1588-1623.
doi: 10.1137/120896219. |
| [14] |
J. A. Gualtieri and R. F. Cromp, Support vector machines for hyperspectral remote sensing classification, Proc. SPIE, 3584 (1999).
doi: 10.1117/12.339824. |
| [15] |
X. Hao, G. Zhang and S. Ma,
Deep learning, International J. Semantic Computing, 10 (2016), 417-439.
doi: 10.1142/S1793351X16500045. |
| [16] |
M. He, B. Li and H. Chen, Deep multi-scale 3D deep convolutional neural network for hyperspectral image classification, 2017 IEEE International Conference on Image Processing (ICIP), (2017), 3904–3908.
doi: 10.1109/ICIP. 2017.8297014. |
| [17] |
W. Hu, Y. Huang, L. Wei, F. Zhang and H. Li,
Deep convolutional neural networks for hyperspectral image classification, J. Sensors, 2015 (2015), 1-12.
doi: 10.1155/2015/258619. |
| [18] |
K. Huang, S. Li, X. Kang and L. Fang, Spectral-spatial hyperspectral image classification based on KNN, Sensing and Imaging, 17 (2016).
doi: 10.1007/s11220-015-0126-z. |
| [19] |
G. Huo, S. X. Yang, Q. Li and Y. Zhou,
A robust and fast method for sidescan sonar image segmentation using nonlocal despeckling and active contour model, IEEE Transactions on Cybernetics, 47 (2017), 855-872.
doi: 10.1109/TCYB.2016.2530786. |
| [20] |
S. Jia, L. Shen and Q. Li,
Gabor feature-based collaborative representation for hyperspectral imagery classification, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 1118-1129.
doi: 10.1109/TGRS.2014.2334608. |
| [21] |
S. Kaewpijit, J. Le Moigne and T. El-Ghazawi,
Automatic reduction of hyperspectral imagery using wavelet spectral analysis, IEEE Transactions on Geoscience and Remote Sensing, 41 (2003), 863-871.
doi: 10.1109/TGRS.2003.810712. |
| [22] |
Y. LeCun, Y. Bengio and G. Hinton,
Deep learning, Nature, 521 (2015), 436-444.
doi: 10.1038/nature14539. |
| [23] |
F. Li, M. K. Ng, T. Y. Zeng and C. Shen,
A multiphase image segmentation method based on fuzzy region competition, SIAM J. Imaging Sci., 3 (2010), 277-299.
doi: 10.1137/080736752. |
| [24] |
G. Li, C. Zhang, F. Gao and X. Zhang,
Doubleconvpool-structured 3D-CNN for hyperspectral remote sensing image classification, J. Image and Graphics, 24 (2019), 639-654.
doi: 10.11834/jig.180422. |
| [25] |
F. Melgani and L. Bruzzone,
Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42 (2004), 1778-1790.
doi: 10.1109/TGRS.2004.831865. |
| [26] |
E. Merkurjev, J. Sunu and A. L. Bertozzi, Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video, 2014 IEEE International Conference on Image Processing (ICIP), (2014), 689–693.
doi: 10.1109/ICIP. 2014.7025138. |
| [27] |
B. Merriman, J. K. Bence and S. J. Osher,
Motion of multiple junctions: A level set approach, J. Comput. Phys., 112 (1994), 334-363.
doi: 10.1006/jcph.1994.1105. |
| [28] |
D. Mumford and J. Shah,
Optimal approximation by piecewise smooth function and associated variational problems, Comm. Pure Appl. Math., 42 (1989), 577-685.
doi: 10.1002/cpa.3160420503. |
| [29] |
M. Myllykoski R. Glowinski, T. Kärkkäinen and T. Rossi,
A new augmented Lagrangian approach for $L^1$-mean curvature image denoising, SIAM J. Imaging Sci., 8 (2015), 95-125.
doi: 10.1137/140962164. |
| [30] |
R. B. Potts, Some generalized order-disorder transformations, in Mathematical Proceedings of the Cambridge Philosophical Society, 48, Cambridge Philosophical Society, 1952, 106–109.
doi: 10.1017/S0305004100027419. |
| [31] |
L. Shen and S. Jia,
Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification, IEEE Transactions on Geoscience and Remote Sensing, 49 (2011), 5039-5046.
doi: 10.1109/TGRS.2011.2157166. |
| [32] |
J. Wang and C.-I. Chang,
Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis, IEEE Transactions on Geoscience and Remote Sensing, 44 (2006), 1586-1600.
doi: 10.1109/TGRS.2005.863297. |
| [33] |
P. Wang and X. Y. Zhu, Model selection of SVM with RBF kernel and its application, Comput Engrg. Appl., (2003), 72–73. |
| [34] |
Y. Wang and L. Wang,
Local Gabor convolutional neural network for hyperspectral image classification, Comput. Sci., 47 (2020), 151-156.
doi: 10.11896/jsjkx.190500147. |
| [35] |
C. Yi, L. F. Zhang and X. Zhang, et al., Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village), J. Remote Sensing, (2019).
doi: 10.11834/jrs. 20209065. |
| [36] |
L. Zhang, H. Sun, Z. Rao and H. Ji, Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 229 (2020).
doi: 10.1016/j. saa. 2019.117973. |
| [37] |
X. Zhang, B. Zhang, L. Zhang and Y. Sun, Hyperspectral remote sensing dataset for tea farm, Global Change Data Repository, (2017).
doi: 10.3974/geodb. 2017.03.04. V1. |
| [38] |
W. Zhu, V. Chayes, A. Tiard and et al.,
Unsupervised classification in hyperspectral imagery with nonlocal total variation and primal-dual hybrid gradient algorithm, IEEE Transactions on Geoscience and Remote Sensing, 55 (2017), 2786-2798.
doi: 10.1109/TGRS.2017.2654486. |
| [39] |
S. Zhuo, X. S. Guo and J. Wan, et al., Fast classification algorithm for polynomial kernel support vector machines, Comput. Engrg., 33 (2007). |
Figure 2.
Classification results of Fanglu data set. The picture of the left is the ground truth, and the remaining four images are the classification result of the SVM, KNN, CNN, and NLVP
Figure 3.
Classification results of Indian data set. The picture of the left is the ground truth, and the remaining four images are the classification result of the SVM, KNN, CNN, and NLVP
Figure 4.
Classification results of Salinas Scene data set. The picture of the left is the ground truth, and the remaining four images are the classification result of the SVM, KNN, CNN, and NLVP
Figure 5.
Classification results of Pavia University data set. The picture of the left is the ground truth, and the remaining four images are the classification result of the SVM, KNN, CNN, and NLVP
Figure 6.
Classification results of XiongAn data set. The picture of the left is the ground truth, and the remaining four images are the classification result of the SVM, KNN, CNN, and NLVP
Table 1.
Comparison of numerical results in the Fanglu and Indian data sets
| Algorithm | Fanglu | Indian | |||
| Run-Time(s) | Accuracy( |
Run-Time(s) | Accuracy( |
||
| SVM | 5.625 | 93.626 | 2.318s | 79.002 | |
| KNN | 121.741 | 93.015 | 11.001s | 66.387 | |
| CNN | 11.002(GPU) | 99.196 | 4.756s(GPU) | 84.590 | |
| NLVP | 16.809 | 99.961 | 37.449 | 99.618 | |
| Algorithm | Fanglu | Indian | |||
| Run-Time(s) | Accuracy( |
Run-Time(s) | Accuracy( |
||
| SVM | 5.625 | 93.626 | 2.318s | 79.002 | |
| KNN | 121.741 | 93.015 | 11.001s | 66.387 | |
| CNN | 11.002(GPU) | 99.196 | 4.756s(GPU) | 84.590 | |
| NLVP | 16.809 | 99.961 | 37.449 | 99.618 | |
Table 2.
Comparison of numerical results in the Salinas and Pavia University data sets
| Algorithm | Salinas | Pavia University | |||
| Run-Time(s) | Accuracy( |
Run-Time(s) | Accuracy( |
||
| SVM | 17.938 | 93.003 | 6.275 | 93.098 | |
| KNN | 283.259 | 89.227 | 90.298 | 86.076 | |
| CNN | 25.804(GPU) | 98.029 | 12.984(GPU) | 99.578 | |
| NLVP | 31.175 | 98.889 | 20.835 | 99.783 | |
| Algorithm | Salinas | Pavia University | |||
| Run-Time(s) | Accuracy( |
Run-Time(s) | Accuracy( |
||
| SVM | 17.938 | 93.003 | 6.275 | 93.098 | |
| KNN | 283.259 | 89.227 | 90.298 | 86.076 | |
| CNN | 25.804(GPU) | 98.029 | 12.984(GPU) | 99.578 | |
| NLVP | 31.175 | 98.889 | 20.835 | 99.783 | |
Table 3.
The accuracy of all algorithms in each category of the Salinas Scene data set
| Label | Samples | SVM( |
KNN( |
CNN( |
NLVP( |
| 1 | 2009 | 97.412 | 94.873 | 99.104 | 99.801 |
| 2 | 3726 | 98.631 | 98.148 | 99.973 | 99.866 |
| 3 | 1976 | 98.330 | 97.217 | 99.241 | 99.798 |
| 4 | 1394 | 98.350 | 98.494 | 100 | 99.785 |
| 5 | 2678 | 97.498 | 95.183 | 99.664 | 98.394 |
| 6 | 3959 | 98.232 | 98.055 | 99.949 | 99.571 |
| 7 | 3579 | 98.044 | 97.346 | 99.721 | 99.749 |
| 8 | 11271 | 89.726 | 84.624 | 95.333 | 97.746 |
| 9 | 6203 | 98.436 | 97.469 | 99.919 | 99.532 |
| 10 | 3278 | 94.356 | 87.340 | 99.847 | 96.827 |
| 11 | 1068 | 96.536 | 88.390 | 98.315 | 97.659 |
| 12 | 1927 | 97.146 | 96.886 | 100 | 100 |
| 13 | 916 | 97.489 | 96.397 | 100 | 98.690 |
| 14 | 1070 | 95.234 | 86.729 | 98.972 | 93.738 |
| 15 | 7268 | 64.364 | 54.100 | 96.863 | 98.005 |
| 16 | 1807 | 97.620 | 95.849 | 99.779 | 97.731 |
| Label | Samples | SVM( |
KNN( |
CNN( |
NLVP( |
| 1 | 2009 | 97.412 | 94.873 | 99.104 | 99.801 |
| 2 | 3726 | 98.631 | 98.148 | 99.973 | 99.866 |
| 3 | 1976 | 98.330 | 97.217 | 99.241 | 99.798 |
| 4 | 1394 | 98.350 | 98.494 | 100 | 99.785 |
| 5 | 2678 | 97.498 | 95.183 | 99.664 | 98.394 |
| 6 | 3959 | 98.232 | 98.055 | 99.949 | 99.571 |
| 7 | 3579 | 98.044 | 97.346 | 99.721 | 99.749 |
| 8 | 11271 | 89.726 | 84.624 | 95.333 | 97.746 |
| 9 | 6203 | 98.436 | 97.469 | 99.919 | 99.532 |
| 10 | 3278 | 94.356 | 87.340 | 99.847 | 96.827 |
| 11 | 1068 | 96.536 | 88.390 | 98.315 | 97.659 |
| 12 | 1927 | 97.146 | 96.886 | 100 | 100 |
| 13 | 916 | 97.489 | 96.397 | 100 | 98.690 |
| 14 | 1070 | 95.234 | 86.729 | 98.972 | 93.738 |
| 15 | 7268 | 64.364 | 54.100 | 96.863 | 98.005 |
| 16 | 1807 | 97.620 | 95.849 | 99.779 | 97.731 |
Table 4.
The accuracy of all algorithms in each category of the Pavia University Scene data set
| Label | Samples | SVM( |
KNN( |
CNN( |
NLVP( |
| 1 | 6631 | 96.139 | 87.830 | 97.994 | 99.774 |
| 2 | 18649 | 96.960 | 98.665 | 99.882 | 100 |
| 3 | 2099 | 74.845 | 64.078 | 94.140 | 99.809 |
| 4 | 3064 | 93.603 | 77.742 | 99.641 | 98.172 |
| 5 | 1345 | 99.628 | 99.257 | 100 | 99.777 |
| 6 | 5029 | 85.942 | 47.345 | 99.920 | 99.980 |
| 7 | 1330 | 80.000 | 82.556 | 91.429 | 99.925 |
| 8 | 3682 | 88.403 | 84.465 | 98.588 | 99.891 |
| 9 | 947 | 100 | 99.789 | 99.578 | 100 |
| Label | Samples | SVM( |
KNN( |
CNN( |
NLVP( |
| 1 | 6631 | 96.139 | 87.830 | 97.994 | 99.774 |
| 2 | 18649 | 96.960 | 98.665 | 99.882 | 100 |
| 3 | 2099 | 74.845 | 64.078 | 94.140 | 99.809 |
| 4 | 3064 | 93.603 | 77.742 | 99.641 | 98.172 |
| 5 | 1345 | 99.628 | 99.257 | 100 | 99.777 |
| 6 | 5029 | 85.942 | 47.345 | 99.920 | 99.980 |
| 7 | 1330 | 80.000 | 82.556 | 91.429 | 99.925 |
| 8 | 3682 | 88.403 | 84.465 | 98.588 | 99.891 |
| 9 | 947 | 100 | 99.789 | 99.578 | 100 |
Table 5.
Comparison of numerical results on the XiongAn data set
| Algorithm | Run-Time(s) | Accuracy( |
| SVM | 1571.159 | 86.361 |
| PCA-SVM | 75.926 | 80.188 |
| PCA-KNN | 515.325 | 80.114 |
| CNN | 135.598(GPU) | 93.037 |
| NLVP | 135.789 | 99.507 |
| Algorithm | Run-Time(s) | Accuracy( |
| SVM | 1571.159 | 86.361 |
| PCA-SVM | 75.926 | 80.188 |
| PCA-KNN | 515.325 | 80.114 |
| CNN | 135.598(GPU) | 93.037 |
| NLVP | 135.789 | 99.507 |
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