Deep Hashing with Triplet Labels and Unification Binary Code Selection for Fast Image Retrieval

Abstract

With the significant breakthrough of computer vision using convolutional neural networks, deep learning has been applied to image hashing algorithms for efficient image retrieval on large-scale datasets. Inspired by Deep Supervised Hashing (DSH) algorithm, we propose to use triplet loss function with an online training strategy that takes three images as training inputs to learn compact binary codes. A relaxed triplet loss function is designed to maximize the discriminability with consideration of the balance property of the output space. In addition, a novel unification binary code selection algorithm is also proposed to represent the scalable binary code in an efficient way, which can fix the problem of conventional deep hashing methods that generate different lengths of binary code by retraining. Experiments on two well-known datasets of CIFAR-10 and NUS-WIDE show that the proposed DSH with use of unification binary code selection can achieve promising performance as compared with conventional image hashing and CNN-based hashing algorithms.