# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Performance on the RSSCN Image Dataset  
To evaluate the effectiveness of proposed approach for scene classification, experiments are conducted on the challenging high resolution remote sensing scene image dataset. The training test ratio of 0.5:0.5 is used for the RSSCN image dataset as is followed in the literature [33]. The training set comprises 1400 stochastically selected images and the remaining images are used to assess the retrieval performance. The optimal retrieval performance is obtained for a visual vocabulary size of 200. As we have used a classification based framework for image retrieval, it is important to note here that the classification accuracy for the proposed RGSIR is 81.44% and the accuracy reported by the dataset creator is 77%. Our proposed representation provides 4.44% higher accuracy compared to the deep learning technique, i.e., the DBN adopted by the [PERSON] et al. [33].  
Table 6 provides a comparison of the retrieval performance of RSSCN with the state-of-the-art image retrieval approaches. We have computed MAP for the top 100 retrievals using the proposed RGSIR. [PERSON] et al. [34] perform an extensive analysis to develop a powerful feature representation to enhance image retrieval. They consider different CNN representative models, i.e., CaffeNet [50],  
Figure 16: Class-wise comparison between of the proposed research with the state-of-the-art methods for OT scene image dataset.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Performance/Method** & **RGSIR** & \begin{tabular}{c} **Visual Words** \\ **Integration** \\ **SIFT-SURF [8]** \\ \end{tabular} & \begin{tabular}{c} **Log Gabor + OC-LBP** \\ **Technique [49]** \\ \end{tabular} & \
\begin{tabular}{c} **Late Fusion (SIFT** \\ **+ TREAK) [26]** \\ \end{tabular} &
\begin{tabular}{c} **Feature Extraction with** \\ **Morphological Operators** \\ \end{tabular} \\ \hline
**MAP** & 82.92 & 69.75 & 63.74 & 63.14 & 60.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the mean average precision using OT-Scene image benchmark.  
VGG-M [51], VGG-VD19 [52] and GoogLeNet [53], in combination with different feature extraction approaches. As our proposed approach is based on mid-level features, we have selected BoW based aggregation methods for comparison. Mid-level features are more resilient to various transformations such as rotation, scale and illumination [34]. The proposed approach provides 16.63% higher accuracy compared to VGG-M (IFK). The proposed RGSIR outperforms the GoogLeNet (BoW), VGG-VD19 (BoW) and CaffeNet (BoW) by 18.56%, 19.2% and 20.88 %, respectively.  
It is important to note here that we have selected the RSSCN image dataset as the images are captured at varying angles and exhibit significant rotation differences. Hence the robustness of the proposed approach to rotation in-variance is also illustrated to some extent. The top 20 retrieval results against the \"Forest\" and \"River & Lake\" semantic categories of the RSSCN image dataset are shown in Figures 17 and 18.