# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 5 Experiments and Discussion
### Comparison with Other Color LBP Descriptor
From this figure, we can deduce that the proposed LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) descriptors not only are efficient for face-based image retrial applications, but also are insensitive to head scales and head turns. In Figure 7b, the {APR, ARR} rate of relational images using OCLBP, IOLBP, maLBP, mdLBP, OC-LBP + CH, LPCP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) are {60%, 60%}, {60%, 60%, 60%}, {60%, 60%, 60%}, {60%, 60%, 60%}, {60%, 60%, 60%}, {80%, 80%}, {100%, 100%}, {100%, 100%} and {100%, 100%} respectively. From this comparison, it can be observed that LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) are robust for object-based image retrial applications when the object of \"toy plane\" is rotated arbitrarily. In Figure 7c, the number of relational images using OCLBP, IOLBP, maLBP, mdLBP, OC-LBP + CH, LPCP, LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) are 6, 6, 6, 8, 7, 6, 6, 6, 10, 10 and 10. From Figure 7c, we summarize that the proposed descriptors are effective and robust for landmark-based image retrial applications even if some occlusions (e.g., tree and car) are purposefully included in images. As expected, from Figure 7d-f, LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) still bring about higher APR rates than existing color LBP descriptors, apart from LPCP in Figure 7d. However, by comparing with the 10 th returned images using LPCP, LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\), it can be clearly seen that the proposed descriptors are more semantically similar with the leftmost query image. From these observations and analyses, the practicability and usability of LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) are illustrated. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Performance**} & \multicolumn{6}{c}{**Date Set**} \\ \cline{3-8}  & & **Face-95** & **ETHZ** & **ZuBuD** & **VisTex** & **KTH-2a** & **Corel-1000** \\ \hline \multirow{2}{*}{OCLBP} & APR (\%) & 64.40 & 42.57 & 56.42 & 92.42 & 90.62 & 68.86 \\  & ARR (\%) & 32.20 & 42.57 & 56.42 & 57.76 & 2.16 & 6.89 \\ \hline \multirow{2}{*}{IOLBP} & APR (\%) & 66.47 & 45.51 & 61.05 & 95.59 & 94.26 & 73.01 \\  & ARR (\%) & 33.24 & 45.51 & 61.05 & 59.75 & 2.25 & 7.30 \\ \hline \multirow{2}{*}{maLBP} & APR (\%) & 67.94 & 55.17 & 59.46 & 95.80 & 92.25 & 74.45 \\  & ARR (\%) & 33.97 & 55.17 & 59.46 & 59.87 & 2.20 & 7.45 \\ \hline \multirow{2}{*}{mdLBP} & APR (\%) & 72.97 & 61.43 & 61.85 & 97.05 & 94.88 & 76.02 \\  & ARR (\%) & 36.49 & 61.43 & 61.85 & 60.65 & 2.26 & 7.60 \\ \hline \multirow{2}{*}{OC-LBP + CH} & APR (\%) & 80.50 & 78.04 & 63.98 & 92.20 & 95.31 & 74.94 \\  & ARR (\%) & 40.25 & 78.04 & 63.98 & 57.63 & 2.27 & 7.49 \\ \hline \multirow{2}{*}{LPCP} & APR (\%) & 92.33 & 88.15 & 84.82 & 98.33 & 98.77 & 82.85 \\  & ARR (\%) & 46.16 & 88.15 & 84.82 & 61.46 & 2.36 & 8.29 \\ \hline \multirow{2}{*}{LTCSP} & APR (\%) & 94.31 & 90.57 & 85.63 & **98.56** & 98.96 & **83.94** \\  & ARR (\%) & 47.15 & 90.57 & 85.63 & **61.60** & 2.36 & **8.39** \\ \hline \multirow{2}{*}{LTCSP\({}_{uni}\)} & APR (\%) & 97.19 & 94.04 & 85.97 & 97.81 & 99.15 & 82.83 \\  & ARR (\%) & 48.60 & 94.04 & 85.97 & 61.13 & 2.37 & 8.28 \\ \hline \multirow{2}{*}{LTCSP\({}_{ri}\)} & APR (\%) & **97.39** & **94.72** & **86.11** & 97.53 & **99.19** & 82.33 \\  & ARR (\%) & **48.69** & **94.72** & **86.11** & 60.96 & **2.37** & 8.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The evaluations of APR and ARR rates obtained by the proposed descriptors and six state-of-the-art color LBP descriptors on six datasets. Figure 7: _Cont._  
Table 6 reports the comparison of the feature dimensionality (d) and the memory cost (kB) comparison among the proposed descriptors and six former color LBP methods.