# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 2 State of the Art Overview
presented a visual semantic enhanced reasoning network (ViSERN) to exploit reasoning between frame regions using the novel random walk rule-based graph convolutional networks for video-text retrieval [21]. They provided experiments on the MSR-VTT [22] and MSVD [23] datasets. [PERSON] et al. proposed a dual deep encoding network that encodes videos and queries into powerful dense representations of their own [24]. These representations can be transformed to perform sequence-to-sequence cross-modal matching effectively given videos as sequences of frames and queries as sequences of words. The authors provided extensive experiments on four video datasets, i.e., MSR-VTT, TRECVID AVS 2016-2018, VATEX [25], and MPII-MD [26]. [PERSON] et al. proposed a framework to retrieve background music for fine-grained short videos using the self-attention and cross-modal attention modules to explore the intra- and the inter-relationshipsof different modalities, respectively [27]. They built and released two virtual-content video datasets, i.e., HoK400 and CFM400 [27]. Although content-based video recognition has made significant progress in recent years, most research has focused on improving accuracy and ignoring real-time efficiency. There are several works on video analysis in the context of big data. [PERSON] et al. provided a review on video big data analytics in the cloud and proposed a service-oriented architecture bridging the gap among large-scale video analytics challenges, big data solutions, and cloud computing [28]. [PERSON] et al. (2019) conducted a survey on multiple object tracking for fast and parallel video processing in MapReduce with the Amazon EC2 Cloud [29]. The results showed that for a large number of videos, the computational speed is faster and the performance is higher when using a fully parallel technique in comparison to a partially parallel technique. The scientific challenge posed for these studies is to solve the best balance between high accuracy and fast response time, especially in large data processing systems. Moreover, a large amount of video data is often required to train and deploy useful machine learning models in industry and entertainment. Smaller enterprises do not have the luxury of accessing enough data for machine learning because of the computational resource limitation. These challenges are critical when developing machine learning algorithms. Several attempts have been made to address the above challenges by using distributed learning techniques such as federated learning over disparate data stores to circumvent the need for centralized data aggregation. This work presents an improved method to train deep neural networks over several data sources in a distributed way, and eliminate the need to centrally aggregate and share the video data. We propose an implementation of content-based video retrieval systems in a parallel and distributed processing environment to improve processing speed and adapt to real-time big data-driven systems. It focuses on the main features, including subtitles, speeches, and objects in video frames. The proposed method allows the training of deep neural networks using video data from multiple nodes in a distributed environment and to secure the representation shared during training. The method was evaluated on existing video data and the performance of this implementation was compared for nine scenarios. This method will pave the way for distributed training of neural networks on privacy-sensitive applications where raw data may not be shared directly or centrally aggregating this data in a data warehouse is not feasible.