Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

Understand

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.

• For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes.
• However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes.
• In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks.