The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

A deep hashing model typically has two main learning objectives: to make the learned
binary hash codes discriminative and to minimize a quantization error. With further …

In this paper, we present a new hashing method to learn compact binary codes for highly
efficient image retrieval on large-scale datasets. While the complex image appearance …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Given the benefits of its low storage requirements and high retrieval efficiency, hashing has
recently received increasing attention. In particular, cross-modal hashing has been widely …

With the rapid growth of image and video data on the web, hashing has been extensively
studied for image or video search in recent years. Benefiting from recent advances in deep …

Deep hashing is an appealing approach for large-scale image retrieval. Most existing
supervised deep hashing methods learn hash functions using pairwise or triple image …

To convert the input into binary code, hashing algorithm has been widely used for
approximate nearest neighbor search on large-scale image sets due to its computation and …

This paper proposes a novel deep polarized network (DPN) for learning to hash, in which
each channel in the network outputs is pushed far away from zero by employing a …

Hash coding has been widely used in the approximate nearest neighbor search for large-
scale image retrieval. Recently, many deep hashing methods have been proposed and …