Recently, the convolutional neural network (CNN)-based hashing method has achieved its
promising performance for image retrieval. However, tackling the discrepancy between …

In this paper, we propose a simple yet effective relaxation-free method to learn more
effective binary codes via policy gradient for scalable image search. While a variety of deep …

Abstract Recently, Convolutional Neural Network (CNN) based hashing method has
achieved its promising performance for image retrieval task. However, tackling the …

With the ever-increasing growth of massive high-dimensional data, deep learning to hash
technology has been widely used for approximate nearest neighbor search on large-scale …

Hashing is one of the most popular and powerful approximate nearest neighbor search
techniques for large-scale image retrieval. Most traditional hashing methods first represent …

Hashing has attracted broad research interests in large scale image retrieval due to its high
search deep hashing methods have been proposed to per form simultaneous nonlinear …

Recently, deep supervised hashing methods have shown state-of-the-art performance by
integrating feature learning and hash codes learning into an end-to-end network to generate …

Recently, deep learning has been successfully applied to the problem of hashing, yielding
remarkable performance compared to traditional methods with hand-crafted features …

Many unsupervised hashing methods are implicitly established on the idea of reconstructing
the input data, which basically encourages the hashing codes to retain as much information …

In this paper, we propose a Deep Hashing method with Active Pairwise Supervision (DH-
APS). Conventional methods with passive pairwise supervision obtain labeled data for …