# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 5 Discussion  
According to the 'no free lunch' theorem [59], there is no single machine learning algorithm that performs best in all application areas. Hence, a variety of decision models should be tested. Therefore, we evaluated the performance of three different classifiers namely SVM, Ensemble subspace \(k\)NN, and ANN. Our experimental results showed that the SVM performance was lower than the performance of Ensemble subspace \(k\)NN and ANN classifiers. Although the quadratic SVM and the Ensemble subspace \(k\)NN produce good classification results, on our dataset ANN showed the best performance results for this proposed system, as shown in Figure 16. Therefore, traffic sign recognition  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Overall Accuracy (\%)** & **Processing Time (s)** \\ \hline
[1] & 97.60 & **-** \\
[15] & 95.71 & 0.43 \\
[17] & 93.60 & **-** \\
[24] & 98.62 & 0.36 \\
[35] & 95.20 & **-** \\
[37] & 92.47 & **-** \\
[57] & 90.27 & 0.35 \\
[58] & 86.70 & **-** \\
**Proposed method** & **99.00** & **0.28** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation between others existing method and proposed method.  
Figure 15: Real-time experimental results.  
system based on the BoW and ANN (instead of BoW and SVM, and BoW and Ensemble subspace \(k\)NN) is recommended for real-time implementation.  
The performance of SVM lies in the choice of kernel [60]. The selection of proper SVM kernel and kernel function parameters, such as width or sigma parameter, may further increase the SVM performance [61; 62; 63]. Moreover, the optimal design for multi-class SVM classifier is still a challenging task for many researchers. There may be various reasons behind the poor performance of Ensemble subspace \(k\)NN classifier including many Ensemble methods for weak learners such as \(k\)NN do not improve the classification performance [64]. Moreover, such Ensemble subspace \(k\)NN is very sensitive to input features of images. Moreover, this classifier assumes that all input features are independent of each other and there is no dependency in the input image features [55]. The other reason behind the poor performance of \(k\)NN is that it does not consider the issue of a soft boundary of the input images where an image appears on either side of a class boundary. There may be several reasons behind the outperformance of ANN classifier. For instance, ANN is a non-parametric model that does not need much statistical computation. Moreover, these decision models are very useful for complex or abstract issues such as image classification. Another reason may be that ANN predicts the best feature weights recursively with the help of input neuron, hidden neurons, and output neurons. This kind of underlying architecture makes ANN a robust classifier compare to other statistical classifiers [65; 66].  
Moreover, in our classification results, we found that the traffic signs belong to class _'No waiting'_ and _'No Stopping'_ showed the highest misclassification error rate in SVM and Ensemble \(k\)NN classifiers. This is because the shape and color of these two signs resemble a lot. Hence, the SVM classifier and Ensemble \(k\)NN classifier could not accurately classify these two signs. In addition, the ANN classier accurately classified _'No Stopping'_ class and also in ANN the misclassification of _'No Waiting'_ class reduced as compared to SVM and Ensemble \(k\)NN. In addition, the traffic sign belonging to class _'Pedestrian Crossing'_ has multiple variations on color and shapes in our dataset, therefore, SVM and Ensemble subspace \(k\)NN classifiers obtained high misclassification error rate. Conversely, ANN classifier achieved reasonable classification accuracy with lowest misclassification error rate for the same _'Pedestrian Crossing'_ class. Another related case is with the class _'No Entry'_ where SVM and Ensemble subspace \(k\)NN classifiers showed high misclassification error results, whereby, ANN obtained the lowest misclassification error rate.