# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Quantization Level \(A_{\text{a}^{*}}\)**} & \multirow{2}{*}{**Quantizer**} & \multicolumn{5}{c}{**Quantization Level \(A_{\text{b}^{*}}\)**} \\ \cline{3-6}  & & \(A_{\text{b}^{*}}=1\) & \(A_{\text{b}^{*}}=2\) & \(A_{\text{b}^{*}}=3\) & \(A_{\text{b}^{*}}=4\) & \(A_{\text{b}^{*}}=5\) \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=1\)} & EICQ & 1363.97 & 1026.78 & 928.64 & 887.69 & 865.82 \\  & FLCQ & 509.07 & 342.97 & 332.88 & 332.43 & 332.41 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=2\)} & EICQ & 946.85 & 609.65 & 511.52 & 470.57 & 448.70 \\  & FLCQ & 283.30 & 117.20 & 107.12 & 106.67 & 106.65 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=3\)} & EICQ & 822.90 & 485.70 & 387.57 & 346.62 & 324.75 \\  & FLCQ & 268.31 & 102.21 & 92.12 & 91.67 & 91.65 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=4\)} & EICQ & 768.42 & 431.23 & 333.09 & 292.14 & 270.27 \\  & FLCQ & 267.57 & 101.47 & 91.39 & 90.94 & 90.92 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=5\)} & EICQ & 741.21 & 404.02 & 305.88 & 264.93 & **243.06** \\  & FLCQ & 267.54 & 101.44 & 91.35 & 90.91 & **90.88** \\ \hline \end{tabular}
\end{table} 
Table 6: The mean square error of the EICQ and FLCQ quantizers on Corel-1000. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Quantization Level \(A_{\text{a}^{*}}\)**} & \multirow{2}{*}{**Quantizer**} & \multicolumn{5}{c}{**Quantization Level \(A_{\text{b}^{*}}\)**} \\ \cline{3-6}  & & \(A_{\text{b}^{*}}=1\) & \(A_{\text{b}^{*}}=2\) & \(A_{\text{b}^{*}}=3\) & \(A_{\text{b}^{*}}=4\) & \(A_{\text{b}^{*}}=5\) \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=1\)} & EICQ & 1299.41 & 1090.02 & 1049.36 & 1031.49 & 1016.96 \\  & FLCQ & 438.11 & 379.63 & 373.66 & 373.47 & 373.47 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=2\)} & EICQ & 838.96 & 629.56 & 588.91 & 571.04 & 556.51 \\  & FLCQ & 176.84 & 118.36 & 112.39 & 112.20 & 112.20 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=3\)} & EICQ & 693.71 & 484.32 & 443.66 & 425.79 & 411.26 \\  & FLCQ & 160.37 & 101.89 & 95.92 & 95.74 & 95.73 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=4\)} & EICQ & 633.55 & 424.16 & 383.50 & 365.64 & 351.10 \\  & FLCQ & 159.52 & 101.04 & 95.07 & 94.89 & 94.88 \\ \hline \multirow{2}{*}{\(A_{\text{a}^{*}}=5\)} & EICQ & 604.17 & 394.77 & 354.12 & 336.25 & **321.71** \\  & FLCQ & 159.49 & 101.01 & 95.03 & 94.85 & **94.84** \\ \hline \end{tabular}
\end{table} 
Table 5: The mean square error of the EICQ and FLCQ quantizers on KTH-2a. that along with the refinement of levels (\(A_{\text{a}^{*}}\), \(A_{\text{b}^{*}}\)), where \(A_{\text{a}^{*}}\), \(A_{\text{b}^{*}}\in\{1,\,2,\,\ldots,5\}\), the MSE values of the EICQ quantizers drops much more obviously, yet the MSE values of the FLCQ quantizers from \(A_{\text{a}^{*}}=4\) to \(5\) are decrease slightly. The results not only illustrate the stability of the FLCQ quantizers, but also demonstrate that it is suitable to stop the quantization level at \(A_{\text{a}^{*}}=5\). Thirdly, under the same quantization interval and level in both quantizers, the MSE values are different from one another among six datasets. These results show that there exist obvious color probability distribution differences among different datasets. So, it is reasonable to adopt the FLCQ quantizer. As a consequence, we can observe that the FLCQ quantizer produces the lower MSE than the EICQ quantizer.