One-Against-All-based multiclass SVM strategies applied to vehicle plate character recognition

Automatic detection of formations using images of oil well drilling cuttings

“…Particularly in this case study, we have 5 classes, so we evaluate 5 hyperplans that each one of those distinguishes a class against others. The test sample belongs to the class which has the maximum sized output from each hyperplane (Mota and Thome, 2009). …”

A Comparison between the Performances of Several Hybrid Methods of Features Extraction for Isolated Printed Tifinagh Characters Recognition

“…In fact recognition for handwritten case is more complex than that printed due to varying writing styles from person to another. In this work we use several efficient techniques in each of the three principal phases forming a certain system of recognition which are firstly the pre-processing then secondly the features extraction then finally learning and In this framework, several studies has been done for recognition of isolated printed Tifinagh character or numerals by using in the features extraction phase the retinal coding method or the moments [1][2][3][4][5] in one hand or in the learningclassification phase the support vectors machines (SVM) [16][17][18][19][20][21][22] on the other hand. Hence, concerning this approach, we are interested to printed Tifinagh characters recognition.…”

A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

“…Therefore a more specialized classification-based biometric system should be approached in order not only to achieve the desired performance improvement, but also to decrease the execution time [1,2]. Commonly used classifiers for different biometrics are support vector machines (SVMs) with different kernels (especially Gaussian and polynomials), Gaussian mixture models-based classifiers, neural networks and multilayer perceptron [5][6][7][8]. Most of them provided significant performance improvements, but their results are strongly dependent on the available datasets [6].…”

“…Commonly used classifiers for different biometrics are support vector machines (SVMs) with different kernels (especially Gaussian and polynomials), Gaussian mixture models-based classifiers, neural networks and multilayer perceptron [5][6][7][8]. Most of them provided significant performance improvements, but their results are strongly dependent on the available datasets [6]. Feature level fusion trained by the SVM-based classifier is used in the proposed work for evaluating the performance of the proposed system.…”

“…Feature level fusion trained by the SVM-based classifier is used in the proposed work for evaluating the performance of the proposed system. The idea behind the SVM is to map the input vectors into a high-dimensional feature space using the "kernel trick" and then to construct a linear decision function in this space so that the dataset becomes separated with a maximum margin [6][7][8][9]. Quite often numbers of feature extraction techniques implemented by multimodal systems are generally equal to the number of biometrics under consideration.…”