Automatic land-cover classification using semi-supervised multilayer perceptron for analyzing remotely sensed images

Chakraborty, Shounak; Adhikary, Sunit Kumar; Roy, Moumita

doi:10.1109/iespc.2017.8071860

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…In addition, we compared our method with traditional decision tree (DT) [32], and multilayer perceptron (MLP) [33]. All the methods were implemented by 10 runs, and the results are shown in Table 3.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The DT [32] obtained an overall accuracy of 73.83± 2.05%. MLP [33] obtained an overall accuracy of 76.25± 3.54%. The details of two basis algorithms were show in Table 4.…”

Section: Methodsmentioning

confidence: 95%

“…ACCURACY OF DT[32] AND MLP[33] 95.83 75.00 70.83 62.50 79.17 R2 62.50 75.00 91.67 75.00 83.33 77.50 R3 70.83 79.17 87.50 79.17 75.00 78.33 R4 79.17 83.33 95.83 83.33 66.67 81.67 R5 70.83 75.00 75.00 62.50 62.50 69.17 R6 75.00 62.50 75.00 87.50 83.33 76.67 R7 87.50 62.50 83.33 62.50 66.67 72.50 R8 83.33 58.33 83.33 70.83 79.17 75.00 R9 79.17 79.17 75.00 79.17 75.00 77.50 R10 83.33 66.67 70.83 79.17 75.00 75.00 Average 76.25± 3.54…”

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confidence: 99%

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Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Liu¹,

Li²,

Quinn³

2018

Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

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To improve the efficiency of stomatology practitioners, this paper proposed a novel teeth type classification method. Our method was based on three successful components: Haar wavelet transform, principal component analysis, and support vector machine. We create a 120-image dataset, with 30 images for incisor, canine, premolar, and molar. The results showed our method achieved an overall classification accuracy of 81.83± 1.79%, better than decision tree and multilayer perceptron methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…The DT [32] obtained an overall accuracy of 73.83± 2.05%. MLP [33] obtained an overall accuracy of 76.25± 3.54%. The details of two basis algorithms were show in Table 4.…”

Section: Methodsmentioning

confidence: 95%

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confidence: 99%

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