4 Steps to Building a Video Search System

The text describes a video search system that uses image similarity to retrieve videos from a repository. It explains the process of converting videos into embeddings, which involves extracting key frames and converting their features into vectors. The workflow includes importing videos using OpenCV library, cutting each video into frames, and inserting extracted vectors (embeddings) into Milvus. For searching, it uses the same VGG model to convert input images into feature vectors and inserts them into Milvus to find similar vectors. It then retrieves corresponding videos from Minio based on Redis correlations. The article also provides a sample dataset of 100,000 GIF files from Tumblr for building an end-to-end solution for video search. Deployment steps are outlined using Docker images and docker-compose.yml configuration file. Finally, the system's interface is displayed, allowing users to input target images and retrieve similar videos.