Advancing Video Search Capabilities: Integrating Feedforward Neural Networks for Efficient Fragment-Based Retrieval

The article focuses on the development of video search technologies. It addresses the challenges in video content analysis and retrieval, especially in the context of rapidly growing video data volumes. The paper presents an innovative system using Deep Convolutional Neural Networks (DCNN) to improve the speed and accuracy of video data processing. This system structures the data processing in several sequential stages, each performed by a separate module, and integrates Feedforward Neural Networks (FFNN) to optimize the search process. The research emphasizes the importance of feature extraction, key frame identification, and abstract vector representation in enhancing video search capabilities.