Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised cross-modal hashing is more flexible and applicable than supervised methods, since no intensive labeling work is involved. However, existing unsupervised methods learn hashing functions by preserving inter and intra co...

We study wear-leveling techniques for cuckoo hashing, showing that it is possible to achieve a memory wear bound of log logn + O(1) after the insertion of n items into a table of sizeCn for a suitable constantC using cuckoo hashing. Moreover, we study our cuckoo hashing method empirically, showing that it significantly improves on the memory wear performance for classic cuckoo hashing and linea...

Hashing is an eÆcient method for storage and retrieval of large amounts of data. Presented here is an evolutionary algorithm to locate eÆcient hashing functions for speci c data sets by sampling and evolving from the set of polynomials. Functions derived in this way show consistently better performance than other common hashing methods, and indicate the power of evolutionary algorithms in searc...

We present a new dynamic hashing scheme for disk-based databases, called Multi-Directory Hashing (MDH). MDH uses multiple hash directories to access a file. The size of each hash directory grows dynamically with the file size. The advantages of MDH are enhanced concurrency, improved bucket utilization and smaller total directory size than single-directory hashing. The expected utilization of MD...

In this paper, we present a new biometric verification system. The proposed system employs a novel biometric hashing scheme that uses our proposed quantization method. The proposed quantization method is based on error-correcting output codes which are used for classification problems in the literature. We improve the performance of the random projection based biometric hashing scheme proposed ...

Hashing methods for fast approximate nearest-neighbor search are getting more and more attention with the excessive growth of the available data today. Embedding the points into the Hamming space is an important question of the hashing process. Analogously to machine learning there exist unsupervised, supervised and semi-supervised hashing methods. In this paper we propose a generic procedure t...

Robust image hashing methods require the robustness to content preserving processing and geometric transform. Zernike moment is a local image feature descriptor whose magnitude components are rotationally invariant and most suitable for image hashing application. In this paper, we proposed Geometric invariant robust image hashing via zernike momment. Normalized zernike moments of an image are u...

UNIX support of disk oriented hashing was originally provided by dbm [ATT79] and subsequently improved upon in ndbm [BSD86]. In AT&T System V, in-memory hashed storage and access support was added in the hsearch library routines [ATT85]. The result is a system with two incompatible hashing schemes, each with its own set of shortcomings. This paper presents the design and performance characteris...

This paper attempts to unify a variety of dynamic hashing methods. Spiral storage, linear hashing and to a certain extent, linear hashing with partial expansions can be seen as particular cases of a more general technique. The approach is closest to spiral storage in concept. A new instantiation of the general method is offered which permits an adjustment to the dynamic growth rate during expan...

Cuckoo hashing is a hash table data structure introduced in [1], that offers constant worst case search time. As a major contribution of this paper, we analyse modified versions of this algorithm with improved performance. Further, we provide an asymptotic analysis of the search costs of all this variants of cuckoo hashing and compare this results with the well known properties of double hashin...