Explosive growth of big data demands efficient and fast algorithms for nearest neighbor
search. Deep learning-based hashing methods have proved their efficacy to learn advanced …

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 …

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 …

Hashing has been widely investigated for large-scale image retrieval due to its search
effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided …

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 …

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 …

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 …