## 4 Experimental Evaluation
### Performance Analysis  
All of the classification results are achieved by utilizing extremely randomised trees (extra-trees) along with the feature extraction by GLACs. In order to evaluate our proposed method, we have employed two benchmark datasets: 8-Scenes and INRIA Holidays. For the 8-Scene dataset, our method shows 86.32% accuracy as shown in Figure 8b. Figure 8a illustrates the performance of our proposed approach after employing GLACs and extra-trees on the 8-Scene dataset. Here the categories like \"open country' and \"streets\" show lower accuracy since the \"open country\" and \"mountain\" categories contain almost the similar scenes. Figure 8b,c illustrate the comparison between GLACs and other feature extraction techniques that include HOG, GIST and SIFT and comparison with existing works for the 8-Scenes dataset. On the other hand, for the INRIA Holidays dataset our approach shows an average accuracy of 88.0%. Finally, Figure 8d, shows the performance of our approach after employing GLACs and extra-trees. These experiments demonstrate a reasonable accuracy of our proposed approach and also that of the selected classifiers for LPaMI. Figure 8: Evaluation of the utilised classifiers for scene/OOI recognition. (**a**) Performance of the proposed method on the 8-Scene dataset using 5-fold cross-validation; (**b**) Comparison between GLACs and other feature extraction techniques on the 8-Scene dataset using 5-fold cross-validation; (**c**) Comparison of the proposed approach with existing works for the 8-scene dataset using 5-fold cross-validation; (**d**) Performance of the proposed approach on the INRIA Holiday dataset using 5-fold cross-validation. GLACs: gradient local auto-correlations; HOG: histogram of oriented gradients; SIFT: scale-invariant feature transform; GOC: gradient orientation consistency. It should be recalled that in the pre-processing stage, we have resized the images to a fixed size of \(384\times 256\). The reason for setting such a low resolution is that the size of the images in the INRIA dataset is 3268 \(\times\) 2448 and that of the images in the 8-Scene dataset is 256 \(\times\) 256. The depth of the input images in both the INRIA dataset and 8-Scene dataset is 24 bits and after converting image from RGB image to gray image the depth becomes 8 bits. In case of INRIA dataset, if we use the original size of the image then the total time required to extract the features using GLACs is approximately 3372.5817 s (average accuracy of 90.1%), and on the other hand if we resize the images to 384 \(\times\) 256, then the total time required to extract the features using GLACs is approximately 302.0094 seconds (average accuracy of 88.0%). Therefore, in order to reduce the feature extraction time, we have reduced the size of the images to a low resolution. For the data migration phase, our aim is to check the scalability of data transfer from HDFS to persistent storage under different constraints. These constraints are the varying types of operations for data, as shown in Table 3. From the results in Figure 9, it is obvious that execution time of data migration improves with an increase in the number of processing nodes for Spark. Hence, we observe a sharp decrease in the execution time in all the datasets except the 8-Scenes dataset. This happens because the size of the 8-Scenes dataset is much smaller than the rest, and hence, data is processed efficiently without much impact on the processing nodes. Next, we observe that the execution time of data migration mode of type-1 is more computationally expensive than the remaining two. We observe the key difference happens due to data normalisation. We perform data normalisation so that there is no redundancy in the data in persistent storage media and to facilitate the graph modelling phase without scanning the entire data.