Nearest neighbor search is a problem of finding the data points from the database such that
the distances from them to the query point are the smallest. Learning to hash is one of the …

Compact coding has been widely applied to approximate nearest neighbor search for large-
scale image retrieval, due to its computation efficiency and retrieval quality. This paper …

Nowadays, due to the exponential growth of user generated images and videos, there is an
increasing interest in learning-based hashing methods. In computer vision, the hash …

Image transmission is gaining importance with the advancement of information
communication technology. Transmission of sensitive images in diverse applications like …

This paper presents a new image hashing that is designed with tensor decomposition (TD),
referred to as TD hashing, where image hash generation is viewed as deriving a compact …

Deep learning to hash improves image retrieval performance by end-to-end representation
learning and hash coding from training data with pairwise similarity information. Subject to …

Existing appearance-based gaze estimation approaches with CNN have poor
generalization performance. By systematically studying this issue, we identify three major …

Deep hashing establishes efficient and effective image retrieval by end-to-end learning of
deep representations and hash codes from similarity data. We present a compact coding …

In the field of computer vision, fine-grained image retrieval is an extremely challenging task
due to the inherently subtle intra-class object variations. In addition, the high-dimensional …

In this paper, we propose a new deep hashing (DH) approach to learn compact binary
codes for scalable image search. Unlike most existing binary codes learning methods, which …