Linear hashing is one of the most attractive dynamic hashing schemes. Linear hashing with partial expansions and linear hashing with priority splitting are two variations with improved space and time performance. Here, we propose a new structure, which is termed linear hashing with partial expansions and priority splitting. The above four structures are compared by simulation and it is shown th...

In this paper we give a generic security proof for hashing modes that make use of an underlying fixed-length permutation. We formulate a set of five simple conditions, which are easy to implement and to verify, for such a hashing mode to be sound. These hashing modes include tree hashing modes and sequential hashing modes. We provide a proof that for any hashing mode satisfying the five conditi...

Previously [SODA’04] we devised the fastest known algorithm for 4-universal hashing. The hashing was based on small pre-computed 4-universal tables. This led to a five-fold improvement in speed over direct methods based on degree 3 polynomials. In this paper, we show that if the pre-computed tables are made 5-universal, then the hash value becomes 5universal without any other change to the comp...

Adaptive hashing with signatures combines the adaptive hashing file structure together with superimposed signatures and several new algorithms to produce a new order-preserving data structure. This new technique has excellent direct retrieval performance, localized index organizations, and improved file index balance. In keeping with the principle advantage of the original adaptive hashing tech...

Similarity search is an important technique in many large scale vision applications. Hashing approach becomes popular for similarity search due to its computational and memory efficiency. Recently, it has been shown that the hashing quality could be improved by combining supervised information, e.g. semantic tags/labels, into hashing function learning. However, tag information is not fully expl...

There is a growing trend in studying deep hashing methods for content-based image retrieval (CBIR), where hash functions and binary codes are learnt using deep convolutional neural networks and then the binary codes can be used to do approximate nearest neighbor (ANN) search. All the existing deep hashing papers report their methods’ superior performance over the traditional hashing methods acc...

Learning based hashing methods, which aim at learning similarity-preserving binary codes for efficient nearest neighbor search, have been actively studied recently. A majority of the approaches address hashing problems for image collections. However, due to the extra temporal information, videos are usually represented by much higher dimensional (thousands or even more) features compared with i...

Image hashing is a potential solution for image content authentication (a desired image hashing algorithm should be robust to common image processing operations and various geometric distortions). In the literature, researchers pay more attention to block-based image hashing algorithms due to their robustness to common image processing operations (such as lossy compression, low-pass filtering, ...

String hashing is a fundamental operation, used in countless applications where fast access to distinct strings is required. In this paper we describe a class of string hashing functions and explore its performance. In particular, using experiments with both small sets of keys and a large key set from a text database, we show that it is possible to achieve performance close to that theoreticall...

A perfect hash function (PHF) is an injective function that maps keys from a set S to unique values, which are in turn used to index a hash table. Since no collisions occur, each key can be retrieved from the table with a single probe. A minimal perfect hash function (MPHF) is a PHF with the smallest possible range, that is, the hash table size is exactly the number of keys in S. MPHFs are wide...