# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 5 Experiments and Discussion
### Comparison with Other Color LBP Descriptor  
Table 5 reports the evaluations of the APR and ARR rates obtained by the proposed descriptors and a series of state-of-the-art color LBP descriptors including OCLBP [18], IOLBP [19], maLBP [23], mdLBP [23], OC-LBP + CH [26], LPCP [27] on all six datasets. The best values are highlighted in bold. For LTCSP, it not only reaches the higher results than all other previous color LBP descriptors on Face-95, ETHZ, ZuBuD and KTH-2a datasets, but also yields the highest {APR = 98.56%, ARR = 61.60%} on VisTex and {APR = 83.94%, ARR = 8.39%} on Corel-1000. In the case of \"LTCSP\({}_{uni}\)\", it achieves a better performance than OCLBP, IOLBP, mdLBP, maLBP, and OC-LBP + CH on all six datasets. For LTCSP\({}_{ri}\), it acquires the highest {APR = 98.39%, ARR = 48.69%} on Face-95, {APR = 94.72%, ARR = 94.72%} on ETHZ, {APR = 86.11%, ARR = 86.11%} on ZuBuD, and {APR = 99.19%, ARR = 2.37%} on KTH-2a respectively. But we also note that the {APR, ARR} rates of LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) are slightly inferior to the LPCP on VisTex and Corel-1000. The main reason is that LPCP has a higher feature dimension. However, the issue can be addressed by adding more useful feature vectors. Based on these considerable results, the effectiveness of the proposed descriptors is demonstrated by comparing with six state-of-the-art color LBP descriptors. Furthermore, there exists illumination differences, head scale and head turn on Face-95, rotation differences on ETHZ, point view differences on ZuBuD, rotation, scaling and illumination differences on KTH-2a. Therefore, the proposed descriptors are also robust against rotation, scaling and illumination to some extent. \begin{table}
\begin{tabular}{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}{*}{LBP} & APR (\%) & 63.45 & 49.28 & 61.45 & 93.37 & 91.56 & 71.86 \\  & ARR (\%) & 31.73 & 49.28 & 61.45 & 58.36 & 2.19 & 7.19 \\ \hline \multirow{2}{*}{LBP\({}_{uni}\)} & APR (\%) & 58.25 & 44.38 & 54.63 & 90.83 & 88.56 & 68.94 \\  & ARR (\%) & 29.12 & 44.38 & 54.63 & 56.77 & 2.11 & 6.89 \\ \hline \multirow{2}{*}{LBP\({}_{ri}\)} & APR (\%) & 59.78 & 45.96 & 53.07 & 89.75 & 85.52 & 66.73 \\  & ARR (\%) & 29.89 & 45.96 & 53.07 & 56.09 & 2.04 & 6.67 \\ \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\({}_{nui}\)} & 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 4: The evaluations the APR and ARR rates resulting from the proposed methods and the LBP-based methods on six datasets. Figure 7 depicts the comparisons of the top-10 returned images acquired by the proposed methods and six previous color LBP methods on six benchmark datasets. Based on the similarity measure score, the top-10 returned images are sorted in descending order. Among them, the leftmost image in each row not only is the most similar image, but also is the query image. Clearly, when a returned image is the relational image, it is tagged in a green box; else it is tagged in a red box. In Figure 7a, the APR rate is 10% using OCLBP, 10% using IOLBP, 10% using maLBP, 20% using mdLBP, 40% using mdLBP, 60% using OC-LBP + CH, 100% using LTCSP, 100% using LTCSP\({}_{uni}\) and 100% using LTCSP\({}_{ri}\) respectively.