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 …

Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …

Supervised deep learning-based hash and vector quantization are enabling fast and large-
scale image retrieval systems. By fully exploiting label annotations, they are achieving …

Conventional unsupervised hashing methods usually take advantage of similarity graphs,
which are either pre-computed in the high-dimensional space or obtained from random …

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 …

Unsupervised hashing is important for indexing huge image or video collections without
having expensive annotations available. Hashing aims to learn short binary codes for …

Recent years have witnessed the wide application of hashing for large-scale image retrieval,
because of its high computation efficiency and low storage cost. Particularly, benefiting from …

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 …

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 …