  As hashing becomes an increasingly appealing technique for large-scale image
retrieval, multi-label hashing is also attracting more attention for the
ability to exploit multi-level semantic contents. In this paper, we propose a
novel deep hashing method for scalable multi-label image search. Unlike
existing approaches with conventional objectives such as contrast and triplet
losses, we employ a rank list, rather than pairs or triplets, to provide
sufficient global supervision information for all the samples. Specifically, a
new rank-consistency objective is applied to align the similarity orders from
two spaces, the original space and the hamming space. A powerful loss function
is designed to penalize the samples whose semantic similarity and hamming
distance are mismatched in two spaces. Besides, a multi-label softmax
cross-entropy loss is presented to enhance the discriminative power with a
concise formulation of the derivative function. In order to manipulate the
neighborhood structure of the samples with different labels, we design a
multi-label clustering loss to cluster the hashing vectors of the samples with
the same labels by reducing the distances between the samples and their
multiple corresponding class centers. The state-of-the-art experimental results
achieved on three public multi-label datasets, MIRFLICKR-25K, IAPRTC12 and
NUS-WIDE, demonstrate the effectiveness of the proposed method.
