Deep Convolutional Neural Network (DCNN) based deep hashing has shown its success for fast and accurate image retrieval, however directly minimizing the quantization error in deep hashing will change the distribution of DCNN features, and consequently change the similarity between the query and the retrieved images in hashing. In this paper, we propose a novel Locality-Constrained Deep Supervis...

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, whi...

In this paper an eecient open addressing hash function called exponential hashing is developed using concepts from dynamical systems theory and number theory. A comparison of exponential hashing versus a widely-used double hash function is performed using an analysis based on Lya-punov exponents and entropy. Proofs of optimal table parameter choices are provided for a number of hash functions. ...

With the rapid decrease in the cost of random access memory (RAM), it will soon become economically feasible to place full-text indexes of a library in main memory. One essential component of the indexing system is a hashing algorithm, which maps a keyword into the memory address of the index information corresponding to that keyword. This thesis studies the application of the minimal perfect h...

A random hash function h is ε-minwise if for any set S, |S| “ n, and element x P S, Prrhpxq “ minhpSqs “ p1 ̆ εq{n. Minwise hash functions with low bias ε have widespread applications within similarity estimation. Hashing from a universe rus, the twisted tabulation hashing of Pǎtraşcu and Thorup [SODA’13] makes c “ Op1q lookups in tables of size u1{c. Twisted tabulation was invented to get good ...

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will lead to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perce...

Interactive Hashing has featured as an essential ingredient in protocols realizing a large variety of cryptographic tasks, notably Oblivious Transfer in the bounded memory model. In Interactive Hashing, a sender transfers a bit string to a receiver such that two strings are received, the original string and a second string that appears to be chosen at random among those distinct from the first....

This is a new technology to support scalable content-based image retrieval (CBIR]), hashing has been recently been focused and future directions of research domain. In this paper, we propose a unique unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea emphatically extracts the rich s...

Many applications use sequences of n consecutive symbols (n-grams). Hashing these n-grams can be a performance bottleneck. For more speed, recursive hash families compute hash values by updating previous values. We prove that recursive hash families cannot be more than pairwise independent. While hashing by irreducible polynomials is pairwise independent, our implementations either run in time ...

This work fulfills sublinear time Nearest Neighbor Search (NNS) in massivescale document collections. The primary contribution is to propose a two-stage unsupervised hashing framework which harmoniously integrates two state-of-theart hashing algorithms Locality Sensitive Hashing (LSH) and Iterative Quantization (ITQ). LSH accounts for neighbor candidate pruning, while ITQ provides an efficient ...