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We propose to use the concept of the Hamming bound to derive the optimalcriteria for learning hash codes with a deep network. In particular, when thenumber of binary hash codes (typically the number of image categories) and codelength are known, it is possible to derive an upper bound on the minimumHamming distance between the hash codes. This upper bound can then be used todefine the loss function for learning hash codes. By encouraging the margin(minimum Hamming distance) between the hash codes of different image categoriesto match the upper bound, we are able to learn theoretically optimal hashcodes. Our experiments show that our method significantly outperforms competingdeep learning-based approaches and obtains top performance on benchmarkdatasets.

Error Correction Maximization for Deep Image Hashing