Scalable object recognition using hierarchical quantization with a vocabulary tree

摘要

An image retrieval technique employing a novel hierarchical feature/descriptor vector quantizer tool—‘vocabulary tree’, of sorts comprising hierarchically organized sets of feature vectors—that effectively partitions feature space in a hierarchical manner, creating a quantized space that is mapped to integer encoding. The computerized implementation of the new technique(s) employs subroutine components, such as: A trainer component of the tool generates a hierarchical quantizer, Q, for application/use in novel image-insertion and image-query stages. The hierarchical quantizer, Q, tool is generated by running k-means on the feature (a/k/a descriptor) space, recursively, on each of a plurality of nodes of a resulting quantization level to ‘split’ each node of each resulting quantization level. Preferably, training of the hierarchical quantizer, Q, is performed in an ‘offline’ fashion.