# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Experimental Results
#### 4.2.1 Corel-1k Dataset  
The Corel-1k dataset has ten categories, and each category has 100 images with a resolution of 256 \(\times\) 384 pixels or 384 \(\times\) 256 pixels. Six samples of each type are shown in Figure 5.  
So far, most of the image retrieval research use Corel-1k as the benchmark for image retrieval and classification. However, it lacks a unified performance evaluation standard, and the experimental results are not convenient for later researchers as a reference. Therefore, this paper synthesizes the parameters used in various methods to explain the data to provide convenience for later researchers. Three comparison experiments with different numbers of retrieved (NR) images are considered. Table 1 lists the APs generated by different methods in each category of the Corel-1k dataset with NR = 20. The methods DELP [10], MCMCM [6], VQ [22], ODBTC [24], CCM-DBPSP [32], CDH-LDP [33], SADWT [14], and C-S-BoW [34] are compared. The proposed Hld method using fused shallow features has better performance than MCMCM, VQ, and CCM-DBPSP. The ObjPlaDctHld method using combined deep features performed much better than Hld and other compared methods, especially in Beaches, Elephants, Mountains, and Food categories. A radar chart of the compared results is shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Average Precision (\%), Top NR = 20**} \\ \cline{2-11}
**Category** & **DELP** & **MCMCM** & **VQ [22]** & **ODBTC** & **CCM-** & **DBPSP** & **SADM** & **CDH-** & **C-S-** & **E**oW** & **Hld** & **ObjPlaDctHld** \\  & **[10]** & **[6]** & & **Vol [22]** & **ODBTC** & **[24]** & **CCM-** & **SDBVP** & **[14]** & **CDH-** & **CO-S-** & **E**oW** & **Hld** & **ObjPlaDctHld** \\ \hline Africans & 74.3 & 69.7 & 70.2 & 84.7 & 70 & 80.1 & 77.9 & **90** & 73.7 & 86.7 \\ Beaches & 65.6 & 54.2 & 44.4 & 46.6 & 56 & 75.3 & 60.1 & 60 & 54.2 & **88.8** \\ Buildings & 75.4 & 63.9 & 70.8 & 68.2 & 57 & 77.1 & 69.1 & **90** & 69.3 & 88 \\ Buses & 98.2 & 89.6 & 76.3 & 88.5 & 87 & 99.2 & 87.6 & 75 & 94.1 & **100** \\ Dinosaurs & 99.1 & 98.7 & **100** & 99.2 & 97 & 99.5 & 99.4 & **100** & 99.9 & **100** \\ Elephants & 63.3 & 48.8 & 63.8 & 73.3 & 67 & 67.8 & 59.2 & 70 & 69.5 & **100** \\ Flowers & 94.1 & 92.3 & 92.4 & 96.4 & 91 & 96.0 & 95.8 & 90 & 92.5 & **100** \\ Horses & 78.3 & 89.4 & 94.7 & 93.9 & 83 & 88.2 & 91.8 & **100** & 91.7 & **99.9** \\ Mountains & 51.3 & 47.3 & 56.2 & 47.4 & 53 & 59.2 & 64 & 70 & 46.3 & **93.8** \\ Food & 85.0 & 70.9 & 74.5 & 80.6 & 74 & 82.3 & 78.1 & 90 & 78 & **97.4** \\ All & 78.46 & 72.5 & 74.3 & 77.9 & 74 & 82.47 & 78.31 & 83 & 76.9 & **95.44** \\ Mean & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different image retrieval methods on the Corel-1k dataset (Top NR is 20). The bold values indicate the best results. Figure 5: Sample images from each category in the Corel-1k dataset. Categories from left to right are Africans, Beaches, Buildings, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains, and Food. Figure 7 shows the APs of the Corel-1k dataset with the NR = 12. Here, the methods of CMSD [35] and MSD-LBP [36] are based on shallow feature fusion. The methods of ENN [27] and LeNetF6 [28] are based on deep features. The proposed Hld method even showed relatively better performance than ENN, LeNetFc6, and CMSD on average. However, most of them showed worse performance in categories such as Beaches and Mountains. There is a big difference in object detection with these scene places and difficulty using low-level features for classification. Figure 8 shows image retrieval results on the Corel-1k dataset by some algorithms. The ObjPlaDctHld method has about 0.3 more improvements on average than Hld when Recall = 1. The shallow features fusion method HLD has better performance than those using a single type of feature. Figure 6: Radar chart of retrieval results using different image retrieval methods on the Corel-1k dataset (Top NR is 20).