# A Cross-Modal Hash Retrieval Method with Fused Triples
## 5 Experiment
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**NUS-WIDE**} \\ \cline{3-5}  & **16 Bit** & **32 Bit** & **64 Bit** \\ \hline \multirow{8}{*}{I\(\rightarrow\)T} & CMFH & 0.384 & 0.390 & 0.406 \\  & CCA-ITQ & 0.396 & 0.382 & 0.370 \\  & SCM & 0.522 & 0.548 & 0.556 \\  & SePH & 0.610 & 0.616 & 0.628 \\  & DCMH & 0.590 & 0.603 & 0.609 \\  & TDH & 0.661 & 0.687 & 0.692 \\  & DLFH & 0.674 & 0.695 & 0.705 \\  & DMSFH & 0.697 & 0.722 & 0.731 \\  & Tri-CMH & 0.768 & 0.780 & 0.791 \\ \hline \multirow{8}{*}{T\(\rightarrow\)I} & CMFH & 0.374 & 0.382 & 0.389 \\  & CCA-ITQ & 0.390 & 0.378 & 0.368 \\ \cline{1-1}  & SCM & 0.516 & 0.540 & 0.549 \\ \cline{1-1}  & SePH & 0.578 & 0.605 & 0.626 \\ \cline{1-1}  & DCMH & 0.638 & 0.651 & 0.657 \\ \cline{1-1}  & TDH & 0.728 & 0.743 & 0.752 \\ \cline{1-1}  & DLFH & 0.780 & 0.802 & 0.821 \\ \cline{1-1}  & DMSFH & 0.709 & 0.752 & 0.766 \\ \cline{1-1}  & Tri-CMH & 0.778 & 0.805 & 0.818 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of methods on the NUS-WIDE dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**MIRFLICKR-25**} \\ \cline{3-5}  & **16 Bit** & **32 Bit** & **64 Bit** \\ \hline \multirow{8}{*}{I\(\rightarrow\)T} & CMFH & 0.576 & 0.574 & 0.569 \\  & CCA-ITQ & 0.576 & 0.574 & 0.569 \\  & SCM & 0.639 & 0.651 & 0.685 \\  & SePH & 0.718 & 0.722 & 0.723 \\  & DCMH & 0.741 & 0.746 & 0.750 \\  & TDH & 0.750 & 0.762 & 0.762 \\  & DLFH & 0.761 & 0.780 & 0.789 \\  & DMSFH & 0.815 & 0.819 & 0.831 \\  & Tri-CMH & 0.838 & 0.848 & 0.856 \\ \hline \multirow{8}{*}{T\(\rightarrow\)I} & CMFH & 0.578 & 0.579 & 0.577 \\  & CCA-ITQ & 0.576 & 0.571 & 0.577 \\ \cline{1-1}  & SCM & 0.655 & 0.670 & 0.698 \\ \cline{1-1}  & SePH & 0.727 & 0.732 & 0.738 \\ \cline{1-1}  & DCMH & 0.782 & 0.790 & 0.793 \\ \cline{1-1}  & TDH & 0.802 & 0.825 & 0.844 \\ \cline{1-1}  & DLFH & 0.825 & 0.851 & 0.870 \\ \cline{1-1}  & DMSFH & 0.807 & 0.823 & 0.856 \\ \cline{1-1}  & Tri-CMH & 0.842 & 0.851 & 0.872 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of methods on the MIRFLICKR-25 dataset. As can be seen from Figure 2, the accuracy of this paper's method is higher when the recall is the initial value on the IAPR TC-12 dataset for text retrieval based on image information and continues to decrease as the recall increases. Until the recall rate is 0.1, the accuracy is lower than that of the DLFHand DMSFH method. However, when the recall reaches 0.25, the decline in accuracy starts to slow down, and the accuracy is higher than the DLFH and DMSFH methods. It is significantly better in the later stages than other methods compared. According to the PR curves principle, when two curves intersect, the performance is determined by the area enclosed by the image and axis; the larger the area, the higher the performance. In cases where it is not easy to determine the area, it can be judged by the intersection of the curve with y = x. The larger the value of the intersection coordinates, the better the performance. Therefore, although there is a situation where the accuracy of a point is lower than that of other methods, the method's accuracy in this paper could be better. This can only mean that the method in this paper was initially only partially adapted to the data set when trained on it. However, soon, the method started to adapt to the data set and reach an optimal accuracy, which was still more excellent than the other methods until the recall was 1. Retrieving images based on textual information differs slightly from retrieving text from images. However, the overall trend is broadly similar, with a significant variation in the early stages, but it quickly adapts well. It can also be seen from the figure that the PR curves of the unsupervised cross-modal retrieval methods CMFH and CCA-ITQ are significantly lower than the other methods. It is sufficient to show that the supervised cross-modal retrieval methods are superior to the unsupervised ones.