Semi-Supervised Metric Learning-Based Anchor Graph Hashing for Large-Scale Image Retrieval

Abstract

Hashing-based image retrieval methods have become a cutting-edge topic in the information retrieval domain due to their high efficiency and low cost. In order to perform efficient hash learning by simultaneously preserving the semantic similarity and data structures in the feature space, this paper presents the semi-supervised metric learning-based anchor graph hashing method. Our proposed approach can be divided into three parts. First, we exploit a transformation matrix to construct the anchor-based similarity graph of the training set. Second, we propose the objective function based on the triplet relationship, in which the optimal transformation matrix can be learned by using the smoothness of labels and the margin hinge loss incurred by the triplet constraint. Moreover, the stochastic gradient descent (SGD) method leverages the gradient on each triplet to update the transformation matrix. Finally, a penalty factor is designed to accelerate the execution speed of SGD. Through comparison with the retrieval results of several state-of-the-art methods on several image benchmarks, the experiments validate the feasibility and advantages of our proposed methods.