## 4 Results and Discussion
### Scenario 2  
Scenario 2 applies _Cuill_ to the standard image datasets like STL-10, CIFAR-10, Caltech101 and Caltech256 and computes the time taken to classify the test images. Using the above-mentioned datasets, 107,647 unlabeled images were collected. 95,000 images were separated for the training process, remaining for testing. The metadata information of the training images was accumulated into a JSON document and each record represents an image. The JSON document is given as input to _uCLUST_ along with the clustering attributes _image_type_, _format_ based on which eight clusters got formed. According to the clusters formed for the input metadata, the unlabeled images are grouped using the identity of each image (_image_name_). Then these grouped images are trained incrementally using _ELM++_ to create models. Then the test images are classified using the incremental models.  
Table 5 lists out the accuracy obtained in testing images when incremental models are created for 2, 4 and 8 clusters using _ELM++_. When the JSON document is clustered with the clustering attribute _format_, 2 clusters are formed (PNG, JPEG) where model _M1_ trains one cluster(PNG) and model _M2_ trains one cluster (JPEG). Incremental testing is done on clusters _1 and 2_ using the two models generated. When the clustering attribute _image_type_ is used, four clusters are formed (ANIMALS, BIRDS, VEHICLES and OTHERS) where model _M1_ trains two clusters(ANIMALS and BIRDS), model _M2_ trains one cluster (VEHICLES) and model _M3_ trains one cluster (OTHERS). Similarly, when the clustering attributes _image_type_, _format_ are used, eight clusters were formed, where model _M1_ trains four clusters, model _M2_ trains two clusters and model _M3_ trains two clusters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attribute** & **No. of** & **Model No.** & **Test** & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-6}
**Used for Clustering** & **Clusters Formed** & **(No. of Clusters)** & **Set** & **Training** & **Incre. Test** \\ \hline \multirow{2}{*}{format} & \multirow{2}{*}{2} & \(M1(1)\) & \multirow{2}{*}{1, 2} & 99 & \multirow{2}{*}{97.75} \\ \cline{3-6}  & & \(M2(1)\) & & 96.5 & \\ \hline \multirow{3}{*}{image\_type} & \multirow{3}{*}{4} & \(M1(2)\) & \multirow{3}{*}{1, 2, 3, 4} & 97 & \multirow{3}{*}{93.3} \\ \cline{3-6}  & & \(M2(1)\) & & 93.3 & \\ \cline{3-6}  & & \(M3(1)\) & & 90.75 & \\ \hline \multirow{3}{*}{image\_type, format} & \multirow{3}{*}{8} & \(M1(4)\) & \multirow{3}{*}{1, 2, 3, 4, 5, 6, 7, 8} & 95.75 & \multirow{3}{*}{94.2} \\ \cline{3-6}  & & \(M2(2)\) & & 98.7 & \\ \cline{3-6}  & & \(M3(2)\) & & 90.75 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance Accuracy of ELM++.  
Figure 10: Comparison of accuracy for MNIST Database.  
Table 6 lists out the performance time of _Cuill_. The table lists the time taken by _uCLUST_ for forming the clusters according to the specified attributes and the training, testing time of _ELM++_. _uCLUST_ is first applied on the metadata to form clusters (number of records is 107,647), on which the training (95,000) is done by _ELM++_. Training time includes the time taken by _ELM++_ to create the training models. Testing of unlabeled images is done only using the generated models. So, testing time does not involve the usage of _uCLUST_ and involves only the time taken to classify the labels of the images using the models generated. The table shows that the testing time reduces when compared to the training time irrespective of the number of clusters formed. The table lists the time taken by _CUIL_ under three cases: (1) using the clustering attribute _format_ in which two clusters were formed, (2) using the clustering attribute _image_type_ in which four clusters were formed, (3) using the clustering attribute _format_, _image_type_ in which eight clusters were formed.