Abstract
In this chapter, we will cover known classifiers that can be used in X-ray testing. Several examples will be presented using Python. The reader can easily modify the proposed implementations in order to test different classification strategies. We will then present how to estimate the accuracy of a classifier using hold-out, cross-validation and leave-one-out. Finally, we will present an example that involves all steps of a pattern recognition problem, i.e., feature extraction, feature selection, classifier’s design, and evaluation. We will thus propose a general framework to design a computer vision system in order to select—automatically—from a large set of features and a bank of classifiers, those features and classifiers that can achieve the highest performance.
Ideal detection of a handgun superimposed onto a laptop (X-ray image \(\mathtt{B0019\_0001}\) colored with ‘sinmap’).
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Notes
- 1.
The available names of models are:
(logistic regression), 
(Minimal Distance), 
(linear discriminant analysis), 
(quadratic discriminant analysis), 
(nearest neighbors), 
(random forest), 
(neural network), 
(AdaBoost), 
(SVM classifier with linear kernel), 
(SVM classifier with RBF kernel). - 2.
Usually, for this end we can use the accuracy metric explained in Sect. 6.3.
- 3.
In pyxvis Library, this classifier is implemented using function
with parameter 
. - 4.
For the configuration of Fig. 6.8 is
. - 5.
In sklearn library, ‘gamma’ defines the influence of the single training examples, ‘C’ is like a regularization parameter in the optimization, and ‘degree’ is the the degree of the polynomial for SVM-POL. See https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html for further details.
- 6.
There are some approaches that define the dictionary as the original samples (see Sparse Representation Classification (SRC) [26]), where \(\mathbf{D}_k = \mathbf{X}_k\).
- 7.
The number of folds v can be another number, for instance 5-fold or 20-fold cross-validation estimate offers very similar performances. In our experiments, we use 10-fold cross-validation because it has become the standard method in practical terms [25].
(logistic regression), 
(Minimal Distance), 
(linear discriminant analysis), 
(quadratic discriminant analysis), 
(nearest neighbors), 
(random forest), 
(neural network), 
(AdaBoost), 
(SVM classifier with linear kernel), 
(SVM classifier with RBF kernel).
with parameter 
.
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Mery, D., Pieringer, C. (2021). Classification in X-Ray Testing. In: Computer Vision for X-Ray Testing. Springer, Cham. https://doi.org/10.1007/978-3-030-56769-9_6
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