# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Influence of Variable Factors on the Experiment
#### 4.3.4 Feature Dimensionality Reduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{**Performance**} & \multicolumn{4}{c}{**Network Architecture, Layer, Dimension**} \\ \cline{3-6}  & & **GoogleNet,** & **GoogleNet,** & **ResNet50,** & **ResNet50,** \\  & & **AvgPool, 1024** & **AvgPool-DCT, 1024** & **AvgPool, 2048** & **AvgPool-DCT, 2048** \\ \hline \multirow{3}{*}{Corel-1k} & AP (\%) & 94.97 & 94.94 (\(\downarrow\)0.03) & 94.67 & 95.28 (\(\uparrow\)0.61) \\  & AR (\%) & 18.99 & 18.99 (\(\rightarrow\)0.00) & 18.93 & 19.06 (\(\uparrow\)0.13) \\ \cline{1-1}  & AP (\%) & 41.12 & 40.90 (\(\downarrow\)0.22) & 46.48 & 45.62 (\(\downarrow\)0.86) \\ \cline{1-1}  & AR (\%) & 8.22 & 8.18 (\(\downarrow\)0.04) & 9.30 & 9.12 (\(\downarrow\)0.18) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: APs and ARs results by applying DCT to deep features, Top NR = 20. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{**Performance**} & \multicolumn{4}{c}{**Network Architecture, Feature, Dimension**} \\ \cline{3-6}  & & **ResNet50,** & **ResNet50,** & **ResNet50,** & **ResNet50,** & **ResNet50,** & **ResNet50,** \\  & & **DCT, 2048** & **DCT, 512** & **DCT, 256** & **DCT, 128** & **DCT, 64** \\ \hline \multirow{3}{*}{Corel-1k} & AP (\%) & 95.28 & 95.25 (\(\downarrow\)0.03) & 94.61 (\(\downarrow\)0.67) & 93.42 (\(\downarrow\)1.86) & 90.46 (\(\downarrow\)4.82) \\  & AR (\%) & 19.06 & 19.05 (\(\downarrow\)0.01) & 18.92 (\(\downarrow\)0.14) & 18.68 (\(\downarrow\)0.38) & 18.09 (\(\downarrow\)0.97) \\ \cline{1-1}  & AP (\%) & 45.62 & 45.02 (\(\downarrow\)0.60) & 43.97 (\(\downarrow\)1.65) & 41.52 (\(\downarrow\)4.10) & 37.43 (\(\downarrow\)8.19) \\ \cline{1-1}  & AR (\%) & 9.12 & 9.00 (\(\downarrow\)0.12) & 8.79 (\(\downarrow\)0.33) & 8.30 (\(\downarrow\)0.82) & 7.49 (\(\downarrow\)1.63) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: APs and ARs results by applying DCT to deep features, Top NR = 20. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{**Performance**} & \multicolumn{4}{c}{**Network Architecture, Feature, Dimension**} \\ \cline{3-6}  & & **ResNet50,** & **ResNet50,** & **ResNet50,** & **ResNet50,** & **ResNet50,** & **ResNet50,** \\  & & **AvgPool, 2048** & **PCA, 512** & **PCA, 256** & **PCA, 128** & **PCA, 64** \\ \hline \multirow{3}{*}{Corel-1k} & AP (\%) & 94.67 & 93.70 (\(\downarrow\)0.97) & 93.82 (\(\downarrow\)0.67) & 94.41 (\(\downarrow\)0.26) & 95.10 (\(\uparrow\)0.43) \\  & AR (\%) & 18.93 & 18.74 (\(\downarrow\)0.19) & 18.76 (\(\downarrow\)0.14) & 18.88 (\(\downarrow\)0.05) & 19.02 (\(\uparrow\)0.09) \\ \cline{1-1}  & AP (  
Comparison of AvgPool-PCA and AvgPool-DCT  
Table 9 lists the advantages and disadvantages of the two feature selection methods. In terms of accuracy, the PCA-based method should be selected. However, considering the independence of features, the features in the dataset should not be used in the image feature extraction process; therefore, we chose the DCT-based method.