A Deep Hashing Learning Network

Understand

Hashing-based methods seek compact and efficient binary codes that preserve the neighborhood structure in the original data space.

• For most existing hashing methods, an image is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to get the compact binary vector.
• Most of the hand-crafted features just encode the low-level information of the input, the feature may not preserve the semantic similarities of images pairs.
• Meanwhile, the hashing function learning process is independent with the feature representation, so the feature may not be optimal for the hashing projection.