K-d Forest Search for Content Identification

The patent divides the large blacklist of images into multiple clusters, where each cluster is represented by a representative object (hash). Instead of comparing uploaded images against every individual image in the blacklist, the system segments the search space by comparing against cluster representatives first, then performing detailed comparison only with members of relevant clusters. This segmentation dramatically improves search speed while maintaining identification accuracy.

The patent introduces hash values as intermediary representations of image clusters. Each cluster of similar images is represented by a hash (representative object), which serves as an intermediary for initial comparison. The hash acts as a mediator that filters out non-matching clusters before performing computationally expensive pixel-level comparisons, thus improving overall system performance.

The patent implements a dynamic k-d forest structure where trees can be dynamically added, removed, and rebalanced. When the blacklist is frequently updated, the system dynamically adjusts the k-d forest structure by inserting new representative objects into appropriate trees or creating new trees when necessary. This dynamic approach maintains tree balance and search performance while allowing continuous adaptation to new content.

The patent performs preliminary organization of new images into clusters and computes their hash representations before inserting them into the k-d forest structure. By pre-processing and pre-organizing data before insertion, the system minimizes disruption to the existing balanced structure, reducing the need for extensive rebalancing operations and maintaining search performance during updates.