# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 5 Experiments
### Experimental Results  
#### 5.4.1 Training Results  
Loss_Value Measure  
In order to calculate and minimize the scenario error, the proposed neural networks are trained by an optimization process base on the loss values. Loss value measures the performance of the models. If the model errors are high, the loss will be high (the model does not do a good job) and vice versa. In Scenarios 4-9, we estimate the values of the classification loss, localization loss, and total loss to evaluate the scenario error. Classification loss measures the predictive inaccuracy of classification models. The localization loss is an error function used to calculate the error value for the predicted boundary box, including the coordinates of the center, width, and height of relative to ground truth box from the models' training data. The total loss is the sum of the two loss functions. Figures 14 and 15 show the histograms of the loss function for Scenarios 4 to 9. From the histograms of the loss function, we can see that the total loss is minimal when the number of training steps increases up to 50,000 for Scenarios 4-6, as represented in Figure 14c,f,i, respectively. Scenarios 4, 5, and 6 had total losses of 0.1, 0.2, and 1.5, respectively. It means that the error of Scenario 4 is the lowest in Scenarios 4-6.  
Figure 15c,f,i show the histograms of the total loss for Scenarios 7, 8, and 9, respectively, after 50,000 training steps. Obviously, the curve of the total loss function in scenario 8 (Figure 15f) is rapidly decreasing to the lowest, less than 0.05, compared to the remaining scenarios and it is stable at the lowest after 50,000 training steps. It means that the error of Scenario 8 is the lowest compared to the remaining scenarios with only 5%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sce** & **Learning** & **Batch** & **No.** & **Scales** & **Max Pooling** & **Epoches** & **IoU** \\ \hline
4 & 0.0003 & 1 & 38 & [0.25, 0.5, 1.0, 2.0] & 3 \(\times\) 3 & 50,000 & 0.5 \\ \hline
5 & 0.0003 & 1 & 38 & [0.25, 0.5, 1.0, 2.0] & 3 \(\times\) 3 & 50,000 & 0.5 \\ \hline
6 & 0.0003 & 24 & 38 & [0.3, 0.95] & 3 \(\times\) 3 & 50,000 & 0.5 \\ \hline
7 & 0.0003 & 1 & 38 & [0.25, 0.5, 1.0, 2.0] & 2 \(\times\) 2 & 100,000 & 0.5 \\ \hline
8 & 0.0003 & 1 & 38 & [0.25, 0.5, 1.0, 2.0] & 2 \(\times\) 2 & 100,000 & 0.5 \\ \hline
9 & 0.0003 & 24 & 38 & [0.3, 0.95] & 2 \(\times\) 2 & 100,000 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for Scenarios 4 to 9.  
Training Run-Time  
All three scenarios (1-3) had no training so training time is 0. The training time for the remaining six scenarios is illustrated in Figure 16. The training time for Scenarios 4 and 7 was 7.2 h and 16.17 h, respectively. For Scenarios 5 and 8, the training time was 6.68 h and 15.68 h, respectively. It was 5.45 h and 8.63 h for Scenarios 6 and 9, respectively. Scenario 7 had the longest training time compared to other scenarios. Scenario 6 was the fastest to train. Although Scenario 8 had a longer training time than Scenarios 4-6, and 9, and its training time was faster than Scenario 7, its error was the lowest. Therefore, we can conclude that Scenario 8 is one of the most suitable scenarios for object feature extraction from images for content-based video retrieval.  
Figure 14: Histograms of the loss values for Scenarios 4, 5, and 6.