# TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data
## 5 Experiments
### Performance Evaluation
#### 5.3.1 Correctness Comparison  
Tables 4 and 5 are the mAP values of each algorithm on the image-to-text retrieval task and the text-to-image retrieval task under different hash code lengths on the MIRFlickr-25k dataset. Tables 6 and 7 are the mAP values of each algorithm on the NUS-WIDE dataset. The best mAP values are displayed in bold font. It is observed from the mAP values in Tables 4-7 that the performance of the TDCMR algorithm outperforms the other ten cross-modal hashing algorithms. \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{6}{*}{Image To Text} & CVH & 0.557 & 0.554 & 0.554 \\  & STMH & 0.602 & 0.608 & 0.605 \\ \cline{1-1}  & CMSSH & 0.585 & 0.584 & 0.572 \\ \cline{1-1}  & CCA & 0.563 & 0.563 & 0.563 \\ \cline{1-1}  & CMFH & 0.579 & 0.581 & 0.583 \\ \cline{1-1}  & SCM & 0.614 & 0.628 & 0.629 \\ \cline{1-1}  & SePH & 0.643 & 0.648 & 0.652 \\ \cline{1-1}  & PRDH & 0.685 & 0.693 & 0.701 \\ \cline{1-1}  & DCMH & 0.698 & 0.705 & 0.711 \\ \cline{1-1}  & TDH & 0.702 & 0.711 & 0.718 \\ \cline{1-1}  & **TDCMR** & **0.711** & **0.719** & **0.727** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison in terms of mAP scores in Image to Text on the MIRFlickr-25k dataset. The highest accuracy is shown in boldface. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Benefit** & **Drawback** \\ \hline \hline \multicolumn{2}{c}{CVH} \\ \hline STMH & High retrieval efficiency in large-scale datasets. & Small datasets cannot give full play to the advantage of high efficiency. \\ \hline \multicolumn{2}{c}{CMSSH} \\ \hline CCA & Relatively effective, easy to train and implement. & It is difficult to simulate the complex correlation of cross media data, \\  & and most can only simulate two media types. \\ \hline \multicolumn{2}{c}{CMFH} & High retrieval efficiency in large-scale datasets. & Small datasets cannot give full play to the advantage of high efficiency. \\ \hline \multicolumn{2}{c}{SCM} & It is helpful for merging different types of information. & The optimization problem with large amount of data is a challenge. \\ \hline \multicolumn{2}{c}{SePH} & More than two media types can be emulated. & Large time and space overhead. \\ \hline \multirow{2}{*}{PRDH} & Providing more accurate information helps to improve the & Large time and space overhead. \\  & retrieval accuracy. & \\ \hline \multirow{2}{*}{DCMH} & Feature learning and hash code learning are combined into & Unsupervised: cannot well overcome the barriers of different modes. \\  & the same framework for the first time. & Instead of manually extracting features, overcome barriers and go \\ \hline \multirow{2}{*}{TDH} & Triplet network to process paired data and unpaired data at & It is difficult to adjust the training parameters due to the supervision of \\  & the same time, and learn feature expression for them. & GAN. \\ \hline \multirow{3}{*}{TDCRM} & The improved triple loss function effectively alleviates the & \\  & semantic gap,Th-quadtree efficiently organizes the spatial & It cannot be directly extended to network-based studies. \\ \cline{1-1}  & information and semantic information of spatial & \\ \cline{1-1}  & multimedia data. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The benefits and drawbacks of various methods. On the MIRFlickr-25k dataset, the performance of the TDCMR algorithm in both retrieval tasks is slightly better than the PRDH and DCMH algorithms. On the NUS-WIDE dataset, the performance of the TDCMR algorithm in both retrieval tasks is also superior to the PRDH and DCMH algorithms. The reason is that PRDH and DCMH algorithms employ pair-wise similarity constraints of category labels for hash learning without considering the relative semantic relationship between samples, which, to some extent, loses rich semantic information and results in limited retrieval accuracy.