Efficient Indexing of Billion-Scale Datasets of Deep Descriptors

CVPR 2016 Artem BabenkoVictor Lempitsky

Existing billion-scale nearest neighbor search systems have mostly been compared on a single dataset of a billion of SIFT vectors, where systems based on the Inverted Multi-Index (IMI) have been performing very well, achieving state-of-the-art recall in several milliseconds. SIFT-like descriptors, however, are quickly being replaced with descriptors based on deep neural networks (DNN) that provide better performance for many computer vision tasks... (read more)

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