# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 5 Experiments and Discussion
### Comparison with LBP-Based Methods  
Table 4 details the evaluations of the APR and ARR rates resulting from the LBP, LBP\({}_{uni}\), and LBP\({}_{ri}\) methods and the proposed LTCSP, LTCSP\({}_{uni}\), and LTCSP\({}_{ri}\) methods. The best {APR, ARR} values are highlighted in bold. First, it can be clearly observed that the proposed LTCSP, LTCSP\({}_{uni}\), and LTCSP\({}_{ri}\) methods achieve remarkable enhancements as compared to the LBP, LBP\({}_{uni}\), and LBP\({}_{ri}\) methods on all six datasets. The foremost reason is that the proposed methods are beneficial to integrate the color information and the LBP information. Secondly, it is noted that LTCSP achieves the highest APR rate on VisTex and Corel-1000, LTCSP\({}_{ri}\) generates the top APR rates on Face-95, ETHZ, ZuBuD, and KTH-2a datasets respectively. The possible reasons are summarized as follows: (1) there is no difference VisTex and Corel-1000; and there exist rotation differences on Face-95, ETHZ, ZuBuD, and KTH-2a. According  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Performance**} & \multicolumn{6}{c}{**Data Set**} \\ \cline{3-8}  & & **Face-95** & **ETHZ** & **ZuBuD** & **VisTex** & **KTH-2a** & **Corel-1000** \\ \hline \multirow{2}{*}{LTCSP} & \((A_{\text{a*}},A_{\text{b*}})\) & (5, 5) & (3, 4) & (5, 5) & **(4, 2)** & (5, 3) & **(3, 3)** \\  & APR (\%) & 94.31 & 90.57 & 85.63 & **98.56** & 98.96 & **83.94** \\ \hline \multirow{2}{*}{LTCSP\({}_{uni}\)} & \((A_{\text{a*}},A_{\text{b*}})\) & (5, 5) & (5, 3) & (5, 4) & (3, 2) & (4, 3) & (3, 2) \\  & APR (\%) & 97.19 & 94.04 & 85.97 & 97.81 & 99.15 & 82.83 \\ \hline \multirow{2}{*}{LTCSP\({}_{ri}\)} & \((A_{\text{a*}},A_{\text{b*}})\) & **(5, 5)** & **(5, 3)** & **(5, 5)** & (3, 3) & **(4, 5)** & (3, 2) \\  & APR (\%) & **97.39** & **94.72** & **86.11** & 97.53 & **99.19** & 82.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The best APR (%) values of LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) with the optimal levels (\(A_{\text{a*}},A_{\text{b*}}\)) over six datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Data Set**} \\ \cline{2-7}  & **Face-95** & **ETHZ** & **ZuBuD** & **VisTex** & **KTH-2a** & **Corel-1000** \\ \hline \multirow{2}{*}{CH} & 6085.91 & 6714.11 & 7483.35 & 6716.29 & 5858.41 & 6386.27 \\  & 1024.93 & 1430.97 & 1622.65 & 1358.55 & 1090.41 & 1276.06 \\ \multirow{2}{*}{CDH} & 391.83 & 205.99 & 90.02 & 255.97 & 487.37 & 370.97 \\ \multirow{2}{*}{MSD} & 385.29 & 200.00 & 83.31 & 249.44 & 481.69 & 365.06 \\  & 48.28 & 64.15 & 17.75 & 57.97 & 95.03 & 92.12 \\ \multirow{2}{*}{LTCSP\({}_{uni}\)} & 48.28 & 64.22 & 17.78 & 58.77 & 95.07 & 102.21 \\  & 48.28 & 64.22 & 17.75 & 58.77 & 94.88 & 102.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean square errors obtained by the hierarchical quantization schemes of different descriptors on six datasets.  
to these encouraging results, it can be deduced that the proposed LTCSP, LTCSP\({}_{uni}\), and LTCSP\({}_{ri}\) methods are superior to the LBP-based methods on all six datasets.