# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Discussion  
Although semantic annotation affects image retrieval performance, the largest improvement in overall performance lies in the recognition algorithm. The standard framework for image retrieval uses BoW and term frequency-inverse document frequency (TF-IDF) to rank images [66]. The image retrieval performance can be enhanced through feature representation, feature quantization, or sorting method. The CNN-SURF Consecutive Filtering and Matching (CSCFM) framework [74] uses the deep feature representation by CNN to filter out the impossible main-brands for narrowing down the range of retrieval. The Deep Supervised Hashing (DSH) method [75] designs a CNN loss function to maximize the discriminability of the output space by encoding the supervised information from the input image pairs. This method takes pairs of images as training inputs and encourages the output of each image to approximate binary code. To solve problems such as repeated patterns for landmark object recognition and clustering, a robust ranking method that considers the burstiness of the visual elements [76] was proposed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**NR**} & \multicolumn{2}{c}{**Corel-1k**} & \multicolumn{2}{c}{**Corel-5k**} & \multicolumn{2}{c}{**Corel-DB80**} \\ \cline{3-8}  & & **AP (\%)** & **AR (\%)** & **AP (\%)** & **AR (\%)** & **AP (\%)** & **AR (\%)** \\ \hline \multirow{2}{*}{**ObjCa**} & NR = 10 & 95.31 & 9.531 & 53.77 & 5.377 & 77.57 & 6.731 \\  & NR = 20 & 93.59 & 18.717 & 46.61 & 9.322 & 71.63 & 12.360 \\  & NR = 10 & 96.21 & 9.621 & 54.66 & 5.466 & 76.96 & 6.671 \\  & NR = 20 & 95.15 & 19.031 & 47.61 & 9.520 & 70.99 & 12.233 \\  & NR = 10 & 96.45 & 9.645 & 55.98 & 5.598 & 79.49 & 6.904 \\  & NR = 20 & 95.15 & 19.029 & 48.77 & 9.754 & 73.51 & 12.703 \\  & NR = 10 & 96.60 & 9.660 & 59.30 & 5.930 & 78.99 & 6.868 \\ \multirow{2}{*}{**ObjPaPaPaHd**} & NR = 20 & 95.43 & 19.087 & 51.22 & 10.244 & 72.51 & 12.543 \\ \hline \hline \end{tabular}
\end{table}
Table 10: APs/ARs of PCA-based method on three datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{DCT-based} & Features are interpretable. The selected feature & \\  & has the characteristic of energy accumulation. & \\  & The features of the query image and the & \\  & characteristics of the dataset are independent & \\  & of each other. & Principal components are not as readable and \\  & Correlated variables are removed. & interpretable as original features. \\ PCA-based & PCA helps in overcoming the overfitting and & \\  & improves algorithm performance. & \\  & & extract the principal components. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Advantages/disadvantages of PCA-based reduction and DCT-based reduction.  
The proposed method is not based on local features, and image matching is not required when retrieving images. When the retrieved image category has not been trained, due to mainly using deep features from classification models, the proposed method is unsuitable for large-scale image dataset retrieval. Shallow features also have obvious limitations for large data set retrieval, and feature representation needs to be strengthened. Based on deep features, additional shallow features cannot significantly improve image retrieval performance and even reduce the overall performance on large data sets. While on the place data sets, the combination of deep features based on scene recognition and deep features of object recognition can improve image retrieval performance as a whole.