# Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
## 4 Experiments and Results
### Backward Subset Selection  
The Backward Subset Selection has a similar behavior. Table 4 shows the results of the Backward Subset Selection of descriptors applied to the whole image (\(BSS1\times 1\)). This is the case with more iterations. It increases the accuracy from 0.917 to 0.937. The resulting descriptor set is composed by BGC2, BGC3, GLCM, LQP, LTP, MTS, STU1, STU2, and WLD.  
Table 5 shows the results of the Backward Subset Selection of descriptors applied to 4 \(\times\) 4 gridded images (\(BSS4\times 4\)). This time, the improvement is from 0.943 to 0.954. The resulting descriptor set is BGC1 BGC2, BGC3, GaborLBP, GLCM, HOG, LQP, LTP, MTS, STU2, and WLD.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** & **Level 6** \\ \cline{2-6}
**Descriptor** & & & & & & D\(\backslash\) \\  & & & & & **Q****X****D** + & **GaborLBP +** \\  & & & & **GaborLBP +** & **HOG +** & **HOG +** \\  & & & & & **BGC1 +** & **LBP +** \\ \hline BGC1 & & 0.92 (SVM) & 0.923 (SVM) & **0.929 (SVM)** & — & — \\ BGC2 & 0.914 (SVM) & 0.92 (SVM) & 0.92 (RF) & 0.937 (SVM) & 0.931 (SVM) \\ BGC3 & 0.917 (SVM) & 0.92 (SVM) & 0.929 (SVM) & 0.934 (SVM) & **0.937 (SVM)** \\ LBP & 0.914 (SVM) & 0.92 (SVM) & 0.926 (RF) & **0.937 (SVM)** & — \\ GaborLBP & **0.92 (SVM)** & — & — & — & — \\ GLCM & 0.914 (SVM) & 0.914 (SVM) & 0.92 (SVM) & 0.929 (SVM) & 0.923 (SVM) \\ HOG & 0.917 (SVM) & 0.903 (SVM) & **0.923 (SVM)** & — & — \\ LQP & 0.917 (RF) & 0.92 (SVM) & 0.923 (SVM) & 0.932 (SVM) & 0.934 (SVM) \\ LTP & 0.909 (SVM) & 0.92 (SVM) & 0.92 (RF) & 0.926 (SVM) & 0.929 (SVM) \\ MTS & 0.914 (SVM) & 0.92 (SVM) & 0.923 (SVM) & 0.929 (SVM) & 0.931 (SVM) \\ STU1 & 0.917 (SVM) & 0.92 (SVM) & 0.923 (RF) & 0.923 (SVM) & 0.926 (SVM) \\ STU2 & 0.914 (SVM) & 0.92 (SVM) & 0.926 (RF) & 0.931 (SVM) & 0.926 (SVM) \\ WLD & 0.914 (SVM) & 0.891 (SVM) & 0.82 (RF) & 0.834 (RF) & 0.934 (RF) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Backward Subset Selection of descriptors applied to 1 \(\times\) 1 gridded image (also known as \(BSS1\times 1\)). Level 1 uses all the descriptors in set D concatenated. Level 2 uses the concatenation of the descriptors in D without each of the descriptors. The following levels use the concatenation of the descriptors in D without the descriptor that makes score higher of the previous level.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Level 1** & **Level 2** & **Level 3** & **Level 4** \\ \cline{2-4}
**Descriptor** & & & & D \(\backslash\) \\  & & & **LBP +** & **STU1 +** \\ \hline BGC1 & & 0.95 (SVM) & 0.951 (SVM) & 0.949 (SVM) \\ BGC2 & & 0.946 (SVM) & 0.951 (SVM) & **0.954 (SVM)** \\ BGC3 & & 0.949 (SVM) & 0.95 (SVM) & 0.945 (SVM) \\ LBP & **0.951 (SVM)** & — & — \\ GaborLBP & & 0.946 (SVM) & 0.951 (RF) & 0.949 (RF) \\ GLCM & & 0.937 (SVM) & 0.937 (SVM) & 0.94 (SVM) \\ HOG & 0.943 (SVM) & 0.94 (SVM) & 0.946 (SVM) \\ LQP & & 0.946 (RF) & 0.946 (SVM) & 0.946 (SVM) \\ LTP & & 0.946 (SVM) & 0.951 (SVM) & 0.949 (SVM) \\ MTS & & 0.946 (SVM) & 0.951 (SVM) & **0.954 (SVM)** \\ STU1 & & 0.946 (SVM) & **0.954 (SVM)** & — \\ STU2 & & 0.949 (SVM) & 0.95 (SVM) & 0.949 (SVM) \\ WLD & & 0.946 (SVM) & 0.946 (SVM) & 0.946 (SVM) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Backward Subset Selection of descriptors applied to 4 \(\times\) 4 gridded image (also known as \(BSS4\times 4\)).  
Table 6, instead, shows the results of the Backward Subset Selection of descriptors applied to \(8\times 8\) gridded images (\(BSS8\times 8\)). The first iteration achieves and score of 0.92, while in the last iteration the score is 0.946. The improvement is 0.026. The resulting descriptor set is BGC1 BGC2, GLCM, HOG, LQP, LTP, MTS, STU1, STU2, and WLD.