A mixture of linear SVMs for multi-class classification of EPPs on hyperspectral image

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“Mixture of Linear SVMs for Multiclass Classification of EPPs on Hyperspectral Image" Patt Khuwuthyakorn1,2,3, Jun Zhou2, Antonio Robles-Kelly2,3

1 Cooperative Research Centre for National Plant Biosecurity 2 Australian National University 3 National ICT Australia (NICTA) biosecurity built on science Cooperative Research Centre for National Plant Biosecurity


Hyperspectral image

Image source: http://www-sipl.technion.ac.il biosecurity built on science


Image source: http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2011.00761.x biosecurity built on science


Previous work  Hyperspectral Image Descriptors  Structural Learning Method for EPPs Detection

Example : Coddling moth

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Mixture of Linear Support Vector Machines (MLSVM) max Y

max Y

min Y

min Y min X

max X Training data

min X

max X MLSVM

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Piece-wise MLSVM max Y

max Y

K=20

min Y

min Y

min X

max X Training data

min X

max X

Piece-wise MLSVM

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Parameter estimation  Maximum Likelihood Estimation(MLE) by optimising log-likelihood function using the EM algorithm.  E-step estimates the value of loglikelihood function  M-step updates parameters for the model biosecurity built on science


About the number of mixtures Accuracy vs number of mixture (4 classes spiral)

Accuracy

100%

Multi-class MLSVM 1

Multi-class MLSVM 2

80% 60% 40% 20% 0% 5

25

45

65

85

Number of mixtures (k)

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Pruning technique by mixture weights

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Random Data

D1-D2

D3-D30 (random)

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Pruning technique by combination coefficients

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D1-D2

D3-D4

D5-D6

D7-D8

D9-D10

D11-D30 biosecurity built on science


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M-MLSVM1

M-MLSVM2

SVM

Qfly

OFM

Medfly

FSBB

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570-580nm 470-480nm

NIR 720+nm

620-630nm

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Acknowledgements  Cooperative Research Centre for National Plant Biosecurity (CRCNPB)  National ICT Australia (NICTA)  CSIRO Entomology  Australian National University (ANU)

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Thank you More information: pattaraporn.khuwuthyakorn@gmail.com

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