Converting raw video into searchable vectors involves preprocessing the video, extracting meaningful features, and encoding those features into vector representations. The goal is to transform unstructured video data into numerical vectors that can be efficiently indexed and queried using similarity search techniques. This process typically uses machine learning models to analyze visual and temporal patterns in the video, then maps those patterns to a compact vector space.
First, the video is preprocessed to extract frames or short clips. For example, you might use a tool like FFmpeg or OpenCV to split a video into individual frames at a specific frame rate (e.g., 1 frame per second). Each frame is then resized or normalized to fit the input requirements of a feature extraction model. If the video includes audio, you might separately process the audio track using spectrograms or speech-to-text models. Next, a pre-trained neural network—such as a CNN (Convolutional Neural Network) for images or a 3D CNN for video clips—is used to extract features. For instance, ResNet-50 or Inception-v3 can generate embeddings for individual frames, while models like C3D or I3D capture temporal features from short video segments. These models output high-dimensional feature vectors that represent the content of the video.
After feature extraction, the vectors are often compressed or aggregated to reduce dimensionality and improve search efficiency. For example, you might use PCA (Principal Component Analysis) or a pooling layer to combine frame-level features into a single video-level vector. These vectors are then stored in a vector database like FAISS, Milvus, or Elasticsearch, which supports efficient nearest-neighbor search. When searching, a query video is processed the same way to generate a vector, and the database returns videos with vectors closest to the query (e.g., using cosine similarity). For instance, a developer could build a system where users upload a video clip of a dog, and the system returns all videos in the database containing similar animals by comparing their vector representations. This approach enables scalable, content-based video retrieval without relying on manual metadata tagging.