# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Experimental Results
#### 4.2.2. Corel-5k Dataset  
The Corel-5k dataset contains 50 categories. Each category contains 100 192 \(\times\) 128 or 128 \(\times\) 192 images in JPEG format. A sample of each category is shown in Figure 9.  
Here, we test the performance of the proposed method using different features, the results are listed in Table 2. The deep features are extracted using ResNet architecture. \"DCT\" refers to the use of front nth DCT coefficients. \"Fc\" indicates that the extracted features come from the fully connected layer.  
Figure 8: Precision vs. recall curves by some methods on the Corel-1k dataset. (**a**) The methods based on deep features; (**b**) The methods based on shallow features.  
Figure 9: Sample image from each category in Corel-5k dataset.  
The experimental results show that using deep features for image retrieval is a significant improvement over shallow features. Fusion features also show better performance than individual features. Among these shallow features, the color feature contributes the most to image retrieval.  
Figure 10 shows Precision-Recall curves corresponding to these methods. Again, the deep feature-based method showed much better performance than the shallow feature-based method.  
Figure 11 shows the APs and ARs on the Corel-5k dataset of the proposed method compared to methods such as MSD [64], MTH [67], CDH [68], HID [69], CPV-THF [70], MSD-LBP [36]. Unfortunately, the results of our proposed algorithm are not the best. Although our method shows better performance than MSD-LBP on the Corel-1k dataset, it lags far behind on the Corel-5k dataset. This is because the deep features are based on many datasets for pre-network training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **Feature Type** & **Feature Length** & **APs (\%)** & **ARs (\%)** \\ \hline HSV histogram & Shallow & 128 & 31.27 & 3.75 \\ HSVCM & Shallow & 9 & 17.72 & 2.13 \\ Quantized & Shallow & 58 & 20.74 & 2.49 \\ LBP-SVD & Shallow & 52 & 16.76 & 2.01 \\ DTCVT-SVD & Shallow-Fusion & 238 & 37.09 & 4.45 \\ ObjDet & Deep, AVG-DCT & 256 & 49.08 & 5.89 \\ PlaDct & Deep, AVG-DCT & 256 & 50.63 & 6.08 \\ ObjFc & Deep, Fc layer & 1000 & 48.19 & 5.78 \\ PlaFc & Deep, Fc layer & 434 & 47.30 & 5.68 \\ ObjTaDct & Deep-Fusion & 512 & 51.63 & 6.20 \\ ObjPlaDctHld & Deep-Shallow-Fusion & 750 & **54.99** & **6.60** \\ \hline \hline \end{tabular}
\end{table}
Table 2: APs and ARs results of using different features on Corel-5k dataset (Top NR = 12).  
Figure 10: Precision-Recall curves on the Corel-5k dataset. (**a**) The methods based on deep features; (**b**) The methods based on shallow features.  
The micro-structure map defined in the MSD method captures the direct relationship between an image's shape and texture features. MTH integrates the co-occurrence matrix and histogram advantages and can capture the spatial correlation of color and texture orientation. HID can capture the internal correlations of different image feature spaces with image structure and multi-scale analysis. The correlated primary visual texton histogram features (CPV-THF) descriptor can capture the correlations among the texture, color, intensity, and local spatial structure information of an image.