  This paper presents a simple yet effective supervised deep hash approach that
constructs binary hash codes from labeled data for large-scale image search. We
assume that the semantic labels are governed by several latent attributes with
each attribute on or off, and classification relies on these attributes. Based
on this assumption, our approach, dubbed supervised semantics-preserving deep
hashing (SSDH), constructs hash functions as a latent layer in a deep network
and the binary codes are learned by minimizing an objective function defined
over classification error and other desirable hash codes properties. With this
design, SSDH has a nice characteristic that classification and retrieval are
unified in a single learning model. Moreover, SSDH performs joint learning of
image representations, hash codes, and classification in a point-wised manner,
and thus is scalable to large-scale datasets. SSDH is simple and can be
realized by a slight enhancement of an existing deep architecture for
classification; yet it is effective and outperforms other hashing approaches on
several benchmarks and large datasets. Compared with state-of-the-art
approaches, SSDH achieves higher retrieval accuracy, while the classification
performance is not sacrificed.
