# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 6 Comparison and Discussion  
In our work, we extract the video contents for retrieval using a combination of three features (subtitles, speeches, and object labels). The experimental results of Scenarios 1 to 9 show that the proposed method for content-based video retrieval achieves a high accuracy from 85% to 96% (Figure 22). In particular, Scenario 8 obtained the highest accuracy of 96% for feature extraction of videos using the proposed distributed deep learning model on Spark. Figures 22 and 23 represent the summary of the scenarios' performance. The experimental results show that the processing time in Spark is shortened by 50% without reducing the accuracy when the dataset is increased by six times compared with a normal computing environment (Scenario 3 versus Scenario 1). Besides, Scenario 8 achieves the highest accuracy compared to the remaining scenarios. The average execution time for Scenario 3 is the lowest because it just extracts the speech and subtitle features. Figure 21: Illustration of object recognition for Scenarios 4–9. Figure 22: Average accuracy for Scenarios 1 to 9. Using the proposed parallel model on Spark, the execution time is improved faster than other regular models as described in the result analysis of Scenarios 1-3. Figure 23 shows a comparison of the run-time of Scenarios 1 to 9. Scenario 7 consumes the most time for feature extraction while Scenario 8 takes a relatively long time but its average accuracy (AP and mAP measures) is the highest. Through experimental scenarios, it emphasizes the advantages of the proposed method using the distributed deep learning model on Spark. It is suitable for large-scale datasets providing reasonable training time with high accuracy. We can conclude that Scenarios 4 and 8 give better results than the remaining scenarios. In addition, some experimental comparisons between the proposed methods and the preceding methods are also made as illustrated in Table 5. We perform subject, object, and speech recognition on several open datasets such as TextCaps ([[https://textvqa.org/textcaps/dataset/](https://textvqa.org/textcaps/dataset/)]([https://textvqa.org/textcaps/dataset/](https://textvqa.org/textcaps/dataset/)), accessed on 20 May 2022), AVSpeech ([[http://festvox.org/cmu_wilderness/VIEVOV/index.html](http://festvox.org/cmu_wilderness/VIEVOV/index.html)]([http://festvox.org/cmu_wilderness/VIEVOV/index.html](http://festvox.org/cmu_wilderness/VIEVOV/index.html)), accessed on 20 May 2022), and Fruits-360 ([[https://public.roboflow.com/classification/fruits-dataset](https://public.roboflow.com/classification/fruits-dataset)]([https://public.roboflow.com/classification/fruits-dataset](https://public.roboflow.com/classification/fruits-dataset)), accessed on 20 May 2022). The results show that our proposed method achieves higher accuracy than the previous methods from 1.4% to 2%. We also provide a combination of two video feature recognitions on the video dataset of Vinh Long Radio and Television Station. Our method achieved a 1.4% to 4.2% higher accuracy than the previous studies. It shows that our proposed method not only improves the processing time with a distributed deep learning environment but also increases the recognition accuracy. We incorporate more video features than previous studies to reduce the possibility of missing information for content-based video retrieval systems.