# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Results  
Tables 2 and 3 show the mAP scores of different compared cross-modal hashing methods at 8 bits, 16 bits, 32 bits, 64 bits, and 128 bits on MIRFlickr and NUS-WIDE datasets, respectively. Note that the mAP metric is one of the comprehensive evaluation criterions used to measure the effectiveness of the proposed method. From these tables, it can be observed that the mAP scores of DSAH are higher than most compared baselines with different code lengths on the two datasets. In the seven compared baselines, only SRLCH, LCMFH, and DCH obtain satisfactory retrieval performance. The main reason is that they learn the common latent representation across different modalities through matrix factorization operations, thus the common latent representation can be used as a bridge to solve the heterogeneous gap between different modalities. However, they ignore the use of an asymmetric learning framework to enhance the semantic similarity of different modalities and the noises contained in the labels. In contrast, our proposed DSAH leverages both the similarity matrix and label information to enhance the semantic information of the learned hash codes, and solves the problem of noises contained in the labels. Specifically, on the MIRFlickr dataset, compared to the best baselines, i.e., SRLCH, the mAP scores of DSAH have an increase of 2.7% on average, and on the NUS-WIDE dataset, DSAH obtains the highest mAP scores of all compared baselines, which demonstrates the efficacy of DSAH. Meanwhile, by comparing supervised cross-modal hashing methods and unsupervised ones on the two datasets, we find that the supervised hashing methods, i.e., SCM-Seq, SePH-km, DCH, LCMFH, and SRLCH, can always outperform the unsupervised hashing methods, i.e., CMFH and FSH, as the supervised information can improve the ability of hash learning process. In addition, the mAP scores of most cross-modal hashing methods increase as the length of the hash codes becomes longer, revealing that the longer codes can handle more discriminative information. The performance on the T2I task, i.e., the use of text modality to retrieve image modality is better than that on the I2T task, i.e., the use of image modality to retrieve text modality. The reason is that the semantic information in text modality is more than that in image modality. \begin{table}
\begin{tabular}{c l l l l l l} \hline
**Task** & **Method** & **8 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **128 Bits** \\ \hline \multirow{6}{*}{I\(\rightarrow\)T} & CMFH & 0.5599 & 0.5687 & 0.5680 & 0.5685 & 0.5687 \\  & FSH & 0.5911 & 0.6016 & 0.6149 & 0.6194 & 0.6242 \\  & SCM-seq & 0.6235 & 0.6373 & 0.6478 & 0.6537 & 0.6611 \\  & SePH-km & 0.6641 & 0.6685 & 0.6818 & 0.6830 & 0.6873 \\  & DCH & 0.6659 & 0.6738 & 0.6859 & 0.6897 & 0.7030 \\  & LCMFH & 0.6821 & 0.6812 & 0.6887 & 0.6909 & 0.7034 \\  & SRLCH & 0.7092 & 0.7113 & 0.7241 & 0.7276 & 0.7359 \\  & DSAH & **0.7156** & **0.7236** & **0.7412** & **0.7498** & **0.7556** \\ \hline \multirow{6}{*}{T\(\rightarrow\)I} & CMFH & 0.5615 & 0.5615 & 0.5606 & 0.5606 & 0.5608 \\  & FSH & 0.5869 & 0.5979 & 0.6114 & 0.6186 & 0.6251 \\  & SCM-seq & 0.6103 & 0.6206 & 0.6298 & 0.6372 & 0.6427 \\  & SePH-km & 0.7033 & 0.7076 & 0.7212 & 0.7293 & 0.7348 \\  & DCH & 0.7256 & 0.7511 & 0.7585 & 0.7681 & 0.7909 \\  & LCMFH & 0.7351 & 0.7308 & 0.7544 & 0.7689 & 0.7806 \\  & SRLCH & 0.7467 & 0.7613 & 0.7798 & 0.7899 & 0.8071 \\  & DSAH & **0.7526** & **0.7782** & **0.8041** & **0.8163** & **0.8180** \\ \hline \end{tabular}
\end{table} 
Table 2: Performance comparison on MIRFlickr dataset measured by mAP.