Abstract：To solve the problem of low retrieval accuracy and long training time in cross-modal retrieval algorithms, a cross-modal retrieval algorithm joining hashing feature and classifier learning (HFCL) is proposed. Uniform hash codes are utilized to describe different modal data with the same semantics. In the training stage, label information is utilized to study discriminative hash codes. And the kernel logistic regression is adopted to learn the hash function of each modal. In the testing stage, for any sample, the hash feature is generated by learned hash function, and another modal datum related to its semantics is retrieved from the database. Experiments on three public datasets verify the effectiveness of HFCL.
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2020, Vol. 33 
