Petrographic image classification using optimized Radial Basis Function Support Vector Machine & validation of its asymptotic behavior

2016 ◽  
Author(s):  
Ashutosh S. Marathe ◽  
Vibha Vyas ◽  
Madhavi Chavhan
Keyword(s):  
Support Vector Machine ◽  
Asymptotic Behavior ◽  
Radial Basis Function ◽  
Image Classification ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis

scholarly journals Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4431
Author(s):  
Abdul Razaque ◽  
Mohamed Ben Haj Frej ◽  
Muder Almi’ani ◽  
Munif Alotaibi ◽  
Bandar Alotaibi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Image Classification ◽  
Basis Function ◽  
State Of The Art ◽  
Support Vector ◽  
Generalization Problem ◽  
Radial Basis

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

scholarly journals Analisis dan Implementasi Metode Gabor Filter dan Support Vector Machine pada Klasifikasi Sidik Jari

2017 ◽  
Vol 2 (2) ◽  
pp. 37
Author(s):  
Intan Raharni Wijaya
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Gabor Filter ◽  
Support Vector ◽  
Kernel Trick ◽  
Radial Basis ◽  
Svm Multiclass

Pengolahan citra digital semakin diminati, salah satunya pada sistem biometrik. Sistem biometrik merupakan sistem dalam pengenalan berdasarkan pola atau ciri khusus yang dimiliki makhluk hidup terutama manusia. Jenis identifikasi biometrik yang umum digunakan adalah pengenalan sidik jari. Sidik jari banyak digunakan dalam kehidupan sehari-hari selama lebih dari 100 tahun karena penerimaan yang tinggi, permanen, akurat, dan keunikan. Kelebihan sidik jari tersebut disebabkan oleh minutiae yang merupakan garis atau guratan pada sidik jari yang berbeda-beda setiap individu. Klasifikasi sidik jari secara umum terbagi menjadi dua tahap yakni ekstraksi fitur serta klasifikasi fitur. <br /> <br /> Ektraksi fitur dapat dilakukan dengan cara filter seperti gabor filter dengan empat sudut orientasi yang berkisar 0, 45, 90 dan 135 derajat. Hasil dari ekstraksi ciri akan klasifikasi dengan tujutan identifikasi. Metode Support Vector Machine (SVM) dapat digunakan sebagai classifier untuk sistem biometrik sidik jari. SVM memiliki kernel trick yang berpengaruh pada akurasi yang dihasilkan. Digunakan SVM multiclass metode one-against-all dalam klasfikasi sidik jari untuk 25 kelas. Akurasi terbesar diperoleh oleh kernel Radial Basis Function (RBF) sebesar 73% untuk data awal dan 76% untuk penambahan data augmentasi

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