# Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
## 4 Experiments and Results  
As stated before, the aim of this paper is to present a method to improve the accuracy on reduced datasets of texture-less objects. In order to prove that our method improves the score of the descriptors by their own, we have created a small dataset composed by seven different random industrial parts (Figure 5). We took 50 pictures of each industrial part taken from different viewpoints and different illumination conditions. Objects are rotated and translated but all images are free from occlusion, and with an empty and white background. Figure 4: Full pipeline of the proposed method, including training, validation, and evaluation. Our pool of descriptors \(D\) for discovering the best combination is made up of BGC1 BGC2, BGC3, LBP, GaborLBP, GLCM, HOG, LQP, LTP, MTS, STU+ (or STU1), STU \(\times\) (or STU2), and WLD. All descriptors but HOG are computed on grids of different sizes: \(1\times 1\), \(4\times 4\), and \(8\times 8\). The length of gridded histograms is the length of the descriptor multiplied by the number of grids. The HOG is applied to the whole image directly. Figure 6 shows a sample image from our database that has been described by each of the descriptors. Figure 5: Pictures of the parts used in the experiment. Figure 6: _Cont._  
The classifiers used are KNN, NB, SVM 1-vs-1 trained with SMO (Sequential Minimal Optimization [39]), SVM 1-again-all trained with SGD (Stochastic Gradient Descent [40]), RC, RF, and Bagging. To distinguish between the two SVM implementations, we call SVM to the one trained with SMO and SVM-SGD to the other one. In terms of performance, some of the classifiers are drastically affected by the parameters, but tuning the parameters makes a complex casuistry which is not the aim of this paper. Used parameters are standards and those are given in the Appendix A. The results are obtained for a Intel Xeon CPU of 3 GHz and 16 GB of RAM, and no GPU acceleration has been used. The following subsections explain the results obtained in the experiments. value of the grid parameter makes not a significant difference in the performance. The recommendation is to use the \(FSS1\times 1\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Level 2**} & \multicolumn{2}{c}{**Level 3**} \\ \cline{3-5}
**Descriptor** & **Level 1** & \multicolumn{2}{c}{**STDU +**} & \multicolumn{2}{c}{**STDU + WLD +**} \\ \hline BGC1 & 0.877 (SVM) & 0.908 (SVM-SGD) & 0.934 (SVM) \\ BGC2 & 0.903 (SVM) & 0.931 (SVM) & **0.969 (SVM)** \\ BGC3 & 0.857 (SVM) & 0.906 (SVM) & 0.931 (SVM) \\ GaborLBP & 0.834 (SVM) & 0.883 (SVM) & 0.903 (SVM) \\ GLCM & 0.923 (RF) & 0.957 (SVM) & 0.966 (SVM) \\ HOG & 0.846 (KNN) & 0.917 (SVM) & 0.94 (SVM) \\ LBP & 0.874 (SVM) & 0.906 (SVM) & 0.929 (SVM) \\ LQP & 0.911 (SVM) & 0.94 (SVM) & **0.969 (SVM)** \\ LTP & 0.889 (SVM) & 0.931 (SVM) & 0.96 (SVM) \\ MTS & 0.909 (SVM) & 0.94 (SVM) & 0.96 (SVM) \\ STU1 & **0.934 (SVM)** & — & — \\ STU2 & 0.914 (SVM) & 0.931 (SVM) & 0.96 (SVM) \\ WLD & 0.931 (SVM) & **0.969 (SVM)** & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Forward Subset Selection of descriptors applied to \(4\times 4\) gridded image (also known as \(FSS4\times 4\)).