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 …

Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …

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 …

Nowadays, blockchain technology and industry has developed rapidly all over the world,
which is inseparable from continuous innovation and improvement on smart contract …

The discrepancy between the cost function used for training a speech enhancement model
and human auditory perception usually makes the quality of enhanced speech …

Existing data-dependent hashing methods usually learn hash functions from pairwise or
triplet data relationships, which only capture the data similarity locally, and often suffer from …

Although deep neural networks are highly effective, their high computational and memory
costs severely hinder their applications to portable devices. As a consequence, lowbit …

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 …

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 …

Cross-modal hashing encodes the multimedia data into a common binary hash space in
which the correlations among the samples from different modalities can be effectively …