online Hash nearest neighbor query method based on data block learning

The invention discloses an online Hash nearest neighbor query method based on data block learning. The method is characterized by comprising the following steps: acquiring and preprocessing image data, defining a Hash model for processing the data, establishing a Hamming distance prediction loss function for judging whether an updated Hash vector is reasonable or not, acquiring an objective function, optimizing the objective function, and performing online Hash nearest neighbor query on given to-be-queried data in a test database; The method is mainly based on a data block thought; processingthe streaming small data blocks each time; an optimization algorithm is designed in a small data space; Compared with the prior art, the method has the advantages that the learning efficiency is improved, the minimum Hamming space loss between data samples in the data blocks is guaranteed in the aspect of the design method, meanwhile, the incremental change of overall online learning is constrained, noise data is effectiv