How are hashing methods like locality-sensitive hashing (LSH) used in video search?

Hashing methods like locality-sensitive hashing (LSH) are used in video search to efficiently find similar videos or segments by reducing the computational complexity of comparing high-dimensional data. Video data, such as frame features or motion vectors, is often represented in high-dimensional spaces, making direct comparisons slow and resource-intensive. LSH addresses this by mapping similar items to the same “hash buckets” with high probability, enabling fast approximate searches. This is particularly useful in large-scale video databases where exact similarity calculations are impractical.

In practice, LSH is applied to video search by first extracting features from video frames or sequences. For example, keyframes might be processed using convolutional neural networks (CNNs) to generate feature vectors, or optical flow techniques could capture motion patterns. These features are then hashed using LSH functions designed to preserve similarity. A common approach is to use random projection-based LSH for cosine similarity, where vectors are projected onto random hyperplanes and binarized into hash codes. During a search, a query video’s features are hashed using the same functions, and only items in matching buckets are compared in detail. This drastically reduces the number of comparisons needed. For instance, a video-sharing platform might use LSH to quickly retrieve clips with similar visual content to a user’s uploaded video, avoiding exhaustive searches across millions of entries.

LSH also handles temporal aspects of video data. For example, temporal segments or shot boundaries can be encoded as sequences of hash codes, allowing LSH to identify videos with similar scene transitions or action sequences. Additionally, LSH can be combined with other techniques, such as inverted indexing, to further optimize retrieval. A specific implementation might involve dividing a video into 5-second chunks, hashing each chunk’s features, and storing them in an LSH-indexed database. When a user searches for a scene, the system hashes the query chunk and retrieves candidate matches from the corresponding buckets, then refines results using a secondary similarity measure. This balance of speed and accuracy makes LSH a practical tool for real-world video search systems.