Abstract
Learning to hash has been recognized to accomplish
highly efficient storage and retrieval for large-scale visual
data. Particularly, ranking-based hashing techniques have
recently attracted broad research attention because ranking accuracy among the retrieved data is well explored and
their objective is more applicable to realistic search tasks.
However, directly optimizing discrete hash codes without
continuous-relaxations on a nonlinear ranking objective
is infeasible by either traditional optimization methods or
even recent discrete hashing algorithms. To address this
challenging issue, in this paper, we introduce a novel supervised hashing method, dubbed Discrete Semantic Ranking
Hashing (DSeRH), which aims to directly embed semantic
rank orders into binary codes. In DSeRH, a generalized
Adaptive Discrete Minimization (ADM) approach is proposed to discretely optimize binary codes with the quadratic
nonlinear ranking objective in an iterative manner and is
guaranteed to converge quickly. Additionally, instead of using 0/1 independent labels to form rank orders as in previous works, we generate the listwise rank orders from the
high-level semantic word embeddings which can quantitatively capture the intrinsic correlation between different categories. We evaluate our DSeRH, coupled with both linear
and deep convolutional neural network (CNN) hash functions, on three image datasets, i.e., CIFAR-10, SUN397 and
ImageNet100, and the results manifest that DSeRH can outperform the state-of-the-art ranking-based hashing methods