# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Performance on Corel-1K Image Dataset  
The Corel-1K image benchmark is extensively used to evaluate CBIR research. To ensure fair comparison experiments, the dataset is stochastically partitioned into training and test subsets with a ratio of 0.5:0.5. The image retrieval performance of the proposed image representation is compared with the existing state-of-the-art CBIR approaches. In order to obtain a sustainable performance, the mean average precision of RGSIR is evaluated by using visual vocabulary of different sizes \([50,100,200,400,600,800]\). The best image retrieval performance for Corel-1K is obtained for a vocabulary of size 600, as can be seen in Figure 9.  
The class-wise comparison obtained from the proposed research in terms of precision and recall is presented in Tables 1 and 2. It can be seen that the proposed approach outperforms the state-of-the-art image retrieval approaches. The proposed RGSIR provides 17.7% higher precision compared to [PERSON] et al. [25]. Our proposed representation outperforms SWBOF [23] by [13.7%, 2.74%] in terms of average precision and recall values for the top 20 retrieval. RGSIR yields [8.23%, 1.65%] higher performance compared to [8] in terms of average retrieval precision and recall values. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Class Name/ Method**} & \multirow{2}{*}{**RGSIR**} & \multirow{2}{*}{**[PERSON] et al. [29]**} & \multicolumn{2}{c}{**Level-1**} & \multicolumn{2}{c}{**Visual Words**} & \multirow{2}{*}{**SWBOF [23]**} & \multirow{2}{*}{**SIFT-LBP [25]**} \\  & & & & & & \\  & & & & & & \\ \hline African People & 72.80 & 76.55 & 73.06 & 60.08 & 64.00 & 57.00 \\ Beach & 69.40 & 63.70 & 69.98 & 60.39 & 54.00 & 58.00 \\ Building & 66.20 & 69.05 & 76.76 & 69.66 & 53.00 & 43.00 \\ Bus & 97.16 & 87.70 & 92.24 & 93.65 & 94.00 & 93.00 \\ Dinosaur & 100.00 & 99.40 & 99.35 & 99.88 & 98.00 & 98.00 \\ Elephant & 80.80 & 91.05 & 81.38 & 70.76 & 78.00 & 58.00 \\ Flower & 94.60 & 91.70 & 83.40 & 88.37 & 71.00 & 83.00 \\ Horse & 90.80 & 95.40 & 82.81 & 82.77 & 93.00 & 68.00 \\ Mountain & 76.20 & 83.40 & 78.60 & 61.08 & 42.00 & 46.00 \\ Food & 86.00 & 65.80 & 82.71 & 65.09 & 50.00 & 53.00 \\
**Mean** & 83.40 & 82.36 & 82.03 & 75.17 & 69.70 & 65.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of precision when using Corel-1K image dataset. Figure 9: Average Precision as a function of vocabulary size. The proposed RGSIR results in [1.04%, 0.2%] higher precision and recall values compared to the work of [PERSON] et al. [29]. Experimental results validate the robustness of the proposed approach against the state-of-the-art retrieval methods. The comparative analysis of the proposed research with the existing state-of-the-art verifies the effectiveness of RGSIR for image retrieval. The average precision depends on the total number of relevant images retrieved, and hence is directly proportional to the number of relevant images retrieved in response to a given query image. It is evident from the Figure that the proposed approach attains the highest number of relevant images against a given query image as compared to the state-of-the-art approaches. Similarly, the average recall is directly proportional to the number of relevant images retrieved to the total number of relevant images of that class present in the dataset. The proposed approach outperforms the state-of-the-art methods by attaining the highest precision and recall values. The _P-R_ curve obtained for the Corel-1K image benchmark is shown in Figure 10. The _P-R_ curve demonstrates the ability of the retrieval system to retrieve relevant images from the image database in an appropriate similarity sequence. The area under the curve illustrates how effectively different methods perform in the same retrieval scenario. The results indicate that the proposed spatial features enhance the retrieval performance as compared to the state-of-the-art image retrieval approaches.