Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …

The explosive growth and widespread accessibility of digital health data have led to a surge
of research activity in the healthcare and data sciences fields. The conventional approaches …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

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 …

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 …

Hashing methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …

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 …

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 …

Thanks to the success of deep learning, cross-modal retrieval has made significant progress
recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to …

Learning to hash has been widely applied to approximate nearest neighbor search for large-
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …