Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.

An exact probability model for finite hash tables

Abstract: The author presents an exact probability model for finite hash tables and applies the model to solve a few problems in the analysis of hashing techniques. The model enables exact computation of table sufficiency index, a parameter useful in the design of small hash tables. The author also presents an exact analysis of the expected length of the longest probe sequence in hashing with separate chaining, and successful search length in infinite uniform hashing giving explicit expressions. It appears that the model can be extended to analyze other hashing schemes such as bounded disorder index method, and to problems in robust data structures, etc. >

A Survey on Image Hashing for Image Authentication

Abstract: The traditional cryptographic hash functions are sensitive to even one-bit difference of the input message. While multimedia data always undergo compression or other signal processing operations, which lead to the unsuitability of multimedia authentication using cryptographic hash. The image hashing has emerged recently which captures visual essentials for robust image authentication. In this paper, we give a comprehensive survey of image hashing. We present an overview of various image hashing schemes and discuss their advantages and limitations in terms of security, robustness, and discrimination under different types of operations on the image.

Supervised Binary Hash Code Learning with Jensen Shannon Divergence

Abstract: This paper proposes to learn binary hash codes within a statistical learning framework, in which an upper bound of the probability of Bayes decision errors is derived for different forms of hash functions and a rigorous proof of the convergence of the upper bound is presented. Consequently, minimizing such an upper bound leads to consistent performance improvements of existing hash code learning algorithms, regardless of whether original algorithms are unsupervised or supervised. This paper also illustrates a fast hash coding method that exploits simple binary tests to achieve orders of magnitude improvement in coding speed as compared to projection based methods.

Weighted Hashing with Multiple Cues for Cell-Level Analysis of Histopathological Images

Abstract: Recently, content-based image retrieval has been investigated for histopathological image analysis, focusing on improving the accuracy and scalability. The main motivation is to interpret a new image (i.e., query image) by searching among a potentially large-scale database of training images in real-time. Hashing methods have been employed because of their promising performance. However, most previous works apply hashing algorithms on the whole images, while the important information of histopathological images usually lies in individual cells. In addition, they usually only hash one type of features, even though it is often necessary to inspect multiple cues of cells. Therefore, we propose a probabilistic-based hashing framework to model multiple cues of cells for accurate analysis of histopathological images. Specifically, each cue of a cell is compressed as binary codes by kernelized and supervised hashing, and the importance of each hash entry is determined adaptively according to its discriminativity, which can be represented as probability scores. Given these scores, we also propose several feature fusion and selection schemes to integrate their strengths. The classification of the whole image is conducted by aggregating the results from multiple cues of all cells. We apply our algorithm on differentiating adenocarcinoma and squamous carcinoma, i.e., two types of lung cancers, using a large dataset containing thousands of lung microscopic tissue images. It achieves \(90.3\,\%\) accuracy by hashing and retrieving multiple cues of half-million cells.

LHlf: lock-free linear hashing (poster paper)

Abstract: LHlf is a new hash table designed to allow very high levels of concurrency. The table is lock free and grows and shrinks auto-matically according to the number of items in the table. Insertions, lookups and deletions are never blocked. LHlf is based on linear hashing but adopts recursive split-ordering of the items within a bucket to be able to split and merge lists in a lock free manner. LHlf is as fast as the best previous lock-free design and in addition it offers stable performance, uses less space, and supports both expansions and contractions.