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IET Image Processing
Hashing has been widely deployed to approximate nearest neighbour search for large-scale multimedia retrieval tasks due to storage and retrieval efficiency. State-of-the-art supervised hashing methods for image retrieval construct deep structures to simultaneously learn image representation and generate good hash codes, and the key step among them is simultaneously learned feature representation and binary hash code. Existing methods use similarity and regularity loss to train deep hashingdoi:10.1049/iet-ipr.2018.6644 fatcat:nwsrjl4x7nedzkhgalsbd5nhv4