# TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data
## 5 Experiments
### Performance Evaluation
#### 5.3.1 Correctness Comparison
The TDCMR algorithm employs the relative semantic relationship between the three samples to build more semantic associations, while the improved triple loss function allows the hash code to retain more category information. To some extent, it overcomes the disadvantage of pairwise similarity constraint, enhances the representation ability of hash code, and, thus, has higher retrieval accuracy. On the MIRFlickr-25k and NUS-WIDE datasets, the retrieval performance of TDCMR, PRDH, and DCMH algorithms based on deep learning is significantly better than those based on artificial features, such as CCA, CMFH, SCM, and SePH. This is because the algorithms based on deep learning can extract deep salient features of data, and the representation ability of deep features are better than artificial features, which shows the superiority of deep neural network in saliency feature extraction. On the MIRFlickr-25k and NUS-WIDE datasets, with the increase of the hash code's length, the retrieval performance of the seven algorithms has been improved to a certain extent. The reason is that as the length of the hash code increases, the richer the semantic information contained in the code, which improves the retrieval accuracy. It should be noted that, on the one hand, the retrieval performance improves with the increase of \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Hash Code Length**} \\ \cline{3-4}  & & **16 Bit** & **32 Bit** & **64 Bit** \\ \hline \multirow{8}{*}{Image To Text} & CVH & 0.400 & 0.392 & 0.386 \\  & STMH & 0.522 & 0.529 & 0.537 \\ \cline{1-1}  & CMSSH & 0.511 & 0.506 & 0.493 \\ \cline{1-1}  & CCA & 0.374 & 0.367 & 0.362 \\ \cline{1-1}  & CMFH & 0.383 & 0.386 & 0.389 \\ \cline{1-1}  & SCM & 0.489 & 0.494 & 0.499 \\ \cline{1-1}  & SePH & 0.531 & 0.534 & 0.542 \\ \cline{1-1}  & PRDH & 0.615 & 0.619 & 0.627 \\ \cline{1-1}  & DCMH & 0.622 & 0.627 & 0.638 \\ \cline{1-1}  & TDH & 0.627 & 0.638 & 0.654 \\ \cline{1-1}  & **TDCMR** & **0.638** & **0.651** & **0.667** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison in terms of mAP scores in Image to Text on the NUS-WIDE dataset. The highest accuracy is shown in boldface. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Hash Code Length**} \\ \cline{3-5}  & & **16 Bit** & **32 Bit** & **64 Bit** \\ \hline \multirow{8}{*}{Text To Image} & CVH & 0.557 & 0.554 & 0.554 \\  & STMH & 0.600 & 0.606 & 0.608 \\ \cline{1-1}  & CMSSH & 0.567 & 0.569 & 0.561 \\ \cline{1-1}  & CCA & 0.564 & 0.563 & 0.563 \\ \cline{1-1}  & CMFH & 0.578 & 0.579 & 0.581 \\ \cline{1-1}  & SCM & 0.611 & 0.618 & 0.620 \\ \cline{1-1}  & SePH & 0.646 & 0.648 & 0.652 \\ \cline{1-1}  & PRDH & 0.728 & 0.732 & 0.739 \\ \cline{1-1}  & DCMH & 0.727 & 0.734 & 0.741 \\ \cline{1-1}  & TDH & 0.736 & 0.742 & 0.749 \\ \cline{1-1}  & **TDCMR** & **0.741** & **0.748** & **0.756** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison in terms of mAP scores in Text to Image on the MIRFlickr-25k dataset. The highest accuracy is shown in boldface. the code length in a certain range; on the other hand, excessive coding length leads to over-fitting and other problems, which reduces the retrieval performance. On the MIRFlickr-25k and NUS-WIDE datasets, the performance of the text-to-image retrieval task is always better than image-to-text retrieval task, probably because the hidden semantic information in image is more difficult to extract, so that the text feature extraction network can learn more information. Figure 5a,b show the accuracy recall curve of each algorithm when using 32-bit hash code on the MIRFlickr-25k dataset, while Figure 6a,b show the accuracy recall curve of each algorithm when using 32-bit hash code on NUS-WIDE dataset. According to the introduction of the accuracy recall curve, the larger the area enclosed by PR curve and coordinate axis, the better retrieval performance algorithm has.