# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 7 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Video Features** & **Method** & **Accuracy** & **Time (s)** & **Dataset** \\ \hline \multirow{4}{*}{**Speech**} & Use the query by example video retrieval [9] & 84.35\% & 7592 & \multirow{4}{*}{**CMU** **Wilderness**} \\ \cline{2-2} \cline{4-4}  & Use CNN-based multimodal networks [13] & 69.78– & & \\  & 88.56\% & 6602 & & \\ \cline{2-2} \cline{4-4}  & Use DeepSpeech [20] & 81.6\% & 8253 & \multirow{2}{*}{**Multilingual Speech**} \\ \cline{2-2} \cline{4-4}  & **Our proposed method** & **90.1\%** & **6272** & \\ \hline \multirow{4}{*}{**Object**} & Use orthogonal polynomials [14] & 69\% & 1047 & \multirow{4}{*}{**Fruits-360**} \\ \cline{2-2} \cline{4-4}  & Use attention-based temporal weighted CNN [15] & 55.9– & & \\ \cline{2-2} \cline{4-4}  & Use a Clip Tracking Network, a Video Tracking Pipeline and a Spatial Temporal Merging [18] & 86.5\% & 88 & \\ \cline{2-2} \cline{4-4}  & Use a novel neural architecture search (NAS) method, termed ViPNAS [19] & 81.7\% & 72 & \multirow{4}{*}{**Fruits-360**} \\ \cline{2-2} \cline{4-4}  & **Our proposed method** & **96\%** & **91** & \\ \hline \multirow{4}{*}{**Object + Speech**} & Use Supervised Deep Canonical Correlation Analysis (S-DCCA) [4] & 85.28\% & 9065 & \multirow{4}{*}{**THVL Dataset**} \\ \cline{2-2} \cline{4-4}  & Use the self-attention (SA) and the cross-modal attention (CMA) modules [27] & 91.6\% & 6,907 & \multirow{4}{*}{**THVL Dataset**} \\ \cline{2-2} \cline{4-4}  & Use a Clip Tracking Network, a Video Tracking Pipeline and a Spatial Temporal Merging [18] & 89.61\% & 7343 & \\ \cline{2-2} \cline{4-4}  & **Our proposed method** & **93\%** & **4879** & \\ \hline \multirow{4}{*}{**Subject + Object**} & Use a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions [21] & 81.3\% & 8263 & \multirow{4}{*}{**THVL Dataset**} \\ \cline{2-2} \cline{4-4}  & Use Dual Encoding, with Transformer features included [24] & 85.9\% & 8296 & \multirow{4}{*}{**THVL Dataset**} \\ \cline{2-2} \cline{4-4}  & **Our proposed method** & **87\%** & **7187** & \\ \cline{2-2} \cline{4-4}  & Use DeepSpeech [20] & 79.8\% & 16,188 & \\ \cline{2-2} \cline{4-4}  & **Our proposed method** & **84\%** & **10,672** & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._between the proposed method and the preceding methods are also made to discuss the advantage of the proposed method. Currently, multimedia data is collected from various sources with many different formats. Processing, transporting, and storing these data, especially videos, are costly. A multimedia information systems need to adapt to the growing big data environment. These challenges form the basis for our proposed approach to content-based video retrieval in a big data processing environment. Many recent studies focus on voice-based or object-based video queries. The novelty in our approach is a combination of the voice, subtitle, and image objects for querying videos with more detailed information. This approach towards building machine learning models that run distributed across multiple computing nodes that can take advantage of distributed storage and computation. Spark's in-memory processing capabilities significantly reduce the cost of data transmission across the network and increase processing speed. The experimental results demonstrate that our proposed method using distributed deep learning on Spark achieves the accuracy of 96% and the processing time is shortened by 50% compared to the other methods without Spark. The proposed method can extract strong video features suitable for big data-driven video retrieval systems. This work can be used as a scientific basis for relevant studies on video processing for real-time big data video retrieval systems. In future directions, we will study methods for extracting associative features related to video content and compare them when deployed across multiple clusters. Conceptualization, A.-C.P. and T.-C.P. ; methodology, A.-C.P. and T.-C.P.