To improve the retrieval efficiency of deep networks, we proposed a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. Our approach constructs hash functions as a latent layer in a deep network. The learned features can help increase the retrieval speed.
Figure: SSDH takes inputs from images and learns image representations, binary codes, and classification through the optimization of an objective function that combines a classification loss with desirable properties of hash codes. The learned codes preserve the semantic similarity between images and are compact for image search.