The geometric hashing is a well-known object recognition technique based on the arrangements of feature points. We have proposed “locally likely arrangement hashing (LLAH)” which outperforms the geometric hashing in both retrieval accuracy and processing time. In this report, by comparing both methods, we consider the major factors which bring the improvement. We also consider the relationship ...

In this paper we consider the case of nonuniform weakly correlated or independent multidimensional record distributions. After demonstrating the advantages of multidimensional hashing schemes without directory, we suggest piecewise linear expansions in order to distribute the load more evenly over the pages of the file. The resuiting piecewise linear order preserving hashing scheme (PLOP-Hashin...

ABSTRACT Minwise hashing is a standard technique in the context of search for approximating set similarities. The recent work [27] demonstrated a potential use of b-bit minwise hashing [26] for batch learning on large data. However, several critical issues must be tackled before one can apply b-bit minwise hashing to the volumes of data often used industrial applications, especially in the cont...

Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow methods, deep can achieve a more satisfactory performance by integrating feature learning hash codes optimizing into same framework. However, most existing either cannot learn unified code for the two correlated data-points of different...

We propose a flexible, fairly general, coding for tree hash modes. The coding does not define a tree hash mode, but instead specifies a way to format the message blocks and chaining values into inputs to the underlying function for any topology, including sequential hashing. The main benefit is to avoid input clashes between different tree growing strategies, even before the hashing modes are d...

The contribution of this thesis is a mathematical analysis a high performance hashing scheme called cuckoo hashing when combined with two very simple and efficient classes of functions that we refer to as the multiplicative class and the linear class, respectively. We prove that cuckoo hashing tends to work badly with these classes. In order to show this, we investigate how the inner structure ...

In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse ...

Hashing is so commonly used in computing that one might expect hash functions to be well understood, and that choosing a suitable function should not be difficult. The results of investigations into the performance of some widely used hashing algorithms are presented and it is shown that some of these algorithms are far from optimal. Recommendations are made for choosing a hashing algorithm and...

A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes within a short Hamming distance. In this paper, we introduce the novel problem of co-hashing where both documents and terms are hashed simultaneously according to their semantic similarities. Fur...