# A Cross-Modal Hash Retrieval Method with Fused Triples
## 5 Experiment
### Experimental Results and Analysis  
First, we analyzed cross-modal retrieval performance in retrieving textual information based on picture and image information based on textual information. We demonstrated the results of mAP using several approaches with varied-length hash codes on different datasets. In order to analyze the performance of different methods, in this paper, some methods of cross-modal hash retrieval were experimentally compared on different datasets. mAP results are shown in Tables 2-4. The mAP results in Table 2 show that this paper's method outperforms other methods, regardless of whether the hash code length is 16, 32, or 64. Among them, the mainstream method DMSFH has the best overall performance on the IAPR-TC12 data set at a hash code length of 32, taking image retrieval text as an example. Compared with it, the method in this paper is 12.5 percentage points higher, with a significant improvement in cross-modal retrieval performance. The performance of CMFH and CCA-ITQ methods based on unsupervised cross-modal retrieval is lower than that of other cross-modal retrieval methods. This indicates that the performance of supervised cross-modal hashing methods on this dataset is generally better than unsupervised cross-modal hashing methods. Furthermore, the lack of label information in the unsupervised cross-modal hashing algorithm is one of the critical reasons for this result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**IAPR TC-12**} \\ \cline{3-5}  & & **16 Bit** & **32 Bit** & **64 Bit** \\ \hline \multirow{6}{*}{I\(\rightarrow\)T} & CMFH & 0.310 & 0.325 & 0.308 \\  & CCA-ITQ & 0.342 & 0.331 & 0.324 \\  & SCM & 0.387 & 0.397 & 0.410 \\  & SePH & 0.449 & 0.452 & 0.471 \\  & DCMH & 0.453 & 0.473 & 0.484 \\  & TDH & 0.462 & 0.477 & 0.498 \\  & DLFH & 0.450 & 0.468 & 0.527 \\  & DMSFH & 0.536 & 0.512 & 0.561 \\  & Tri-CMH & 0.629 & 0.637 & 0.648 \\ \hline \multirow{6}{*}{T\(\rightarrow\)I} & CMFH & 0.325 & 0.330 & 0.319 \\  & CCA-ITQ & 0.342 & 0.332 & 0.324 \\ \cline{1-1}  & SCM & 0.364 & 0.361 & 0.369 \\ \cline{1-1}  & SePH & 0.446 & 0.458 & 0.473 \\ \cline{1-1}  & DCMH & 0.518 & 0.537 & 0.546 \\ \cline{1-1}  & TDH & 0.535 & 0.584 & 0.569 \\ \cline{1-1}  & DLFH & 0.481 & 0.509 & 0.602 \\ \cline{1-1}  & DMSFH & 0.589 & 0.612 & 0.615 \\ \cline{1-1}  & Tri-CMH & 0.631 & 0.642 & 0.653 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of methods on the IAPR TC-12 dataset. As seen from the mAP results in Table 3, there is a clear advantage in the effectiveness of all methods on this data set, indicating that most methods adapt to the MIRFLICKR-25 data set. Among them, the mainstream method DMSFH has the best overall performance on this data set at a hash code length of 32 for image retrieval text, for example. Compared with it, the method in this paper is 2.9 percentage points higher, with a significant improvement in cross-modal retrieval performance. Again, similar to the IAPR TC-12 data set results, the two unsupervised cross-modal hash retrieval methods performed significantly weaker than the other supervised cross-modal hash retrieval methods.