A survey on indexing techniques for big data: taxonomy and performance evaluation
The explosive growth in volume, velocity, and diversity of data produced by mobile devices
and cloud applications has contributed to the abundance of data or 'big data.'Available …
and cloud applications has contributed to the abundance of data or 'big data.'Available …
Large-scale retrieval for medical image analytics: A comprehensive review
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 …
digital imaging techniques, where huge amounts of medical images were produced with …
Supervised discrete hashing
Recently, learning based hashing techniques have attracted broad research interests due to
the resulting efficient storage and retrieval of images, videos, documents, etc. However, a …
the resulting efficient storage and retrieval of images, videos, documents, etc. However, a …
Deep supervised hashing for fast image retrieval
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 …
efficient image retrieval on large-scale datasets. While the complex image appearance …
A survey on learning to hash
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 …
the distances from them to the query point are the smallest. Learning to hash is one of the …
Hashnet: Deep learning to hash by continuation
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 …
scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep …
Supervised hashing with kernels
Recent years have witnessed the growing popularity of hashing in large-scale vision
problems. It has been shown that the hashing quality could be boosted by leveraging …
problems. It has been shown that the hashing quality could be boosted by leveraging …
Simultaneous feature learning and hash coding with deep neural networks
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-
scale image retrieval tasks. For most existing hashing methods, an image is first encoded as …
scale image retrieval tasks. For most existing hashing methods, an image is first encoded as …
An insight into extreme learning machines: random neurons, random features and kernels
GB Huang - Cognitive Computation, 2014 - Springer
Extreme learning machines (ELMs) basically give answers to two fundamental learning
problems:(1) Can fundamentals of learning (ie, feature learning, clustering, regression and …
problems:(1) Can fundamentals of learning (ie, feature learning, clustering, regression and …
Deep hashing network for efficient similarity retrieval
Due to the storage and retrieval efficiency, hashing has been widely deployed to
approximate nearest neighbor search for large-scale multimedia retrieval. Supervised …
approximate nearest neighbor search for large-scale multimedia retrieval. Supervised …