# Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
## 4 Experiments and Results
### Comparative between Methods  
Figure 7 shows a comparative of the highest scores of each iteration of the different selection techniques. The maximum of each technique is obtained in the previous to the last level.  
On general, Forward Subset Selection achieves better results than Backward Subset Selection. Backward selection computation time is higher than forward so is preferable to use a forward selection since computation time is shorter and the performance is better.  
We have compared our method with two known CNN methods: Xception [41] and Siamese [42]. Xception is a Deep learning network inspired by Inception [43], where Inception modules, treated as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** \\ \cline{2-5}
**Descriptor** & **D** & **D\(\backslash\)** & **D\(\backslash\)** & **D\(\backslash\)** & **D\(\backslash\)** \\  & & & **LDP +** & **LBP +** \\  & & & **LBP +** & **GaborLBP +** & **GaborLBP +** \\ \hline BGC1 & & 0.914 (SVM) & 0.923 (SVM) & 0.943 (SVM) & 0.931 (SVM) \\ BGC2 & & 0.909 (SVM) & 0.929 (SVM) & 0.934 (SVM) & 0.943 (SVM) \\ BGC3 & & 0.917 (SVM) & 0.926 (SVM) & **0.946 (SVM)** & **—** \\ LBP & & **0.926 (SVM)** & **—** & **—** & **—** \\ GaborLBP & & 0.92 (SVM) & **0.934 (RF)** & **—** & **—** \\ GLCM & & 0.894 (SVM) & 0.9 (SVM) & 0.917 (SVM) & 0.929 (SVM) \\ HOG & 0.92 (SVM) & 0.903 (SVM) & 0.914 (SVM) & 0.934 (SVM) & **0.946 (SVM)** \\ LQP & & 0.906 (RF) & 0.917 (SVM) & 0.931 (SVM) & 0.934 (SVM) \\ LTP & & 0.909 (SVM) & 0.914 (SVM) & 0.929 (SVM) & 0.934 (SVM) \\ MTS & & 0.909 (SVM) & 0.937 (SVM) & 0.929 (SVM) & **0.946 (SVM)** \\ STU1 & & 0.906 (SVM) & 0.929 (SVM) & 0.934 (SVM) & 0.937 (SVM) \\ STU2 & & 0.914 (SVM) & 0.929 (SVM) & 0.937 (SVM) & 0.943 (SVM) \\ WLD & & 0.906 (SVM) & 0.923 (SVM) & 0.931 (SVM) & 0.943 (SVM) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Backward Subset Selection of descriptors applied to \(8\times 8\) gridded image (also known as \(BSS8\times 8\)).  
Figure 7: Comparative of highest F1 of each iteration of the Subset Selection techniques. FSS stands for Forward Subset Selection and BSS stands for Backward Subset Selection. The numbers after each selection technique stand for the number of windows the descriptor has been applied to.  
intermediate step in-between regular convolutions, are replaced by depthwise separable convolutions. Siamese network, instead, is a Convolutional network that inputs two images and classifies if the two images are the same object. One of its advantages is that it gives good results even with small datasets.  
Table 7 shows a comparison between the proposed method and the two previous described methods. In the case of the Xception, the results are not as good as Siamese or our proposal as Xception works better for large datasets. Even if Siamese works better than Xception, it does not give better results than our proposal.  
In terms of speed, Figure 8 shows a comparative of the test time for each of the methods. The time shown is the average of the different descriptors and classification techniques for testing one image. \(FSS1\times 1\), \(FSS4\times 4\), and \(BSS1\times 1\) have a low computation time, and \(BSS8\times 8\) version performs much slower than the rest of versions due to the high dimensionallity of the data.  
Taking into account the speed and score the best option is to use \(FSS1\times 1\). Although score is similar to the rest of the versions, it outperforms remaining in terms of speed.