How do you index large video databases for efficient search?

Indexing large video databases for efficient search requires converting raw video content into searchable formats while balancing accuracy and performance. The process typically involves extracting meaningful features, organizing metadata, and using specialized storage systems. The goal is to enable fast similarity searches or keyword-based queries without scanning every frame of every video.

First, feature extraction transforms video content into numerical representations. For visual content, techniques like convolutional neural networks (CNNs) can identify objects, scenes, or motion patterns. For example, a pre-trained CNN like ResNet might generate feature vectors for keyframes sampled from a video. Audio tracks can be processed using spectrograms or embeddings from speech recognition models. Temporal features, such as optical flow or shot boundaries, help capture changes over time. These features are stored as vectors in a database optimized for high-dimensional data, such as FAISS or Annoy. By indexing these vectors, you can perform similarity searches (e.g., “find clips with a sunset”) using nearest-neighbor algorithms.

Second, metadata and annotations complement raw features. This includes manual tags (e.g., “sports,” “interview”), automatic captions from speech-to-text models, or timestamps for events detected by object detectors (e.g., “car appears at 00:12”). Structured metadata like video duration, resolution, or geolocation can be indexed in relational databases (e.g., PostgreSQL) or search engines like Elasticsearch. For example, a query like “videos longer than 5 minutes filmed in Tokyo” can be resolved quickly using metadata indexes. Combining metadata with feature vectors allows hybrid queries, such as filtering by location first and then searching for specific visual patterns within those results.

Finally, efficient storage and retrieval rely on partitioning and compression. Videos are often split into shorter segments (e.g., 10-second clips) to reduce the granularity of searches. Feature vectors are compressed using techniques like product quantization to save memory. For real-time applications, in-memory databases like Redis cache frequently accessed data. To scale horizontally, distributed systems like Apache Solr or Milvus cluster indexes across servers. For example, a video platform might shard its index by upload date, allowing parallel searches across shards. Regularly pruning outdated or low-quality content from the index also improves performance over time.