In recent years, deep hashing methods have been proved to be efficient since it employs convolutional neural network to learn features and hashing codes simultaneously. However, these methods are mostly supervised. In real-world application, it is a time-consuming and overloaded task for annotating a large number of images. In this paper, we propose a novel unsupervised deep hashing method for ...

This article presents a simple algorithm for packing sparse 2-D arrays into minimal I-D arrays in O(r?) time. Retrieving an element from the packed I-D array is O(l). This packing algorithm is then applied to create minimal perfect hashing functions for large word lists. Many existing perfect hashing algorithms process large word lists by segmenting them into several smaller lists. The perfect ...

In this paper, we are concerned with the problem of designing optimal linear hashing files for orthogonal range retrieval. Through the study of performance expressions, we show that optimal basic linear hashing files and optimal recursive linear hashing files for orthogonal range retrieval can be produced, in certain cases, by a greedy method called the MMI (minimum marginal increase) method; a...

Hashing is a popular solution to Approximate Nearest Neighbor (ANN) problems. Many hashing schemes aim at preserving the Euclidean distance of the original data. However, it is the geodesic distance rather than the Euclidean distance that more accurately characterizes the semantic similarity of data, especially in a high dimensional space. Consequently, manifold based hashing methods have achie...

Hashing techniques have been widely applied for large scale similarity search problems due to the computational and memory efficiency. However, most existing hashing methods assume data examples are independently and identically distributed. But there often exists various additional dependency/structure information between data examples in many real world applications. Ignoring this structure i...

Hashing has witnessed an increase in popularity over the past few years due to the promise of compact encoding and fast query time. In order to be effective hashing methods must maximally preserve the similarity between the data points in the underlying binary representation. The current best performing hashing techniques have utilised supervision. In this paper we propose a two-step iterative ...