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Feature hashing

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


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Journal ArticleDOI
TL;DR: Experimental results show that the improved perceptual hashing-based tracking algorithms perform favorably against the state-of-the-art algorithms under various challenging environments in terms of time cost, accuracy and robustness.
Abstract: Video object tracking represents a very important computer vision domain. In this paper, a perceptual hashing based template-matching method for object tracking is proposed to efficiently track objects in challenging video sequences. In the tracking process, we first apply three existing basic perceptual hashing techniques to visual tracking, namely average hash (aHash), perceptive hash (pHash) and difference hash (dHash). Compared with previous tracking methods such as mean-shift or compressive tracking (CT), perceptual hashing-based tracking outperforms in terms of efficiency and accuracy. In order to further improve the accuracy of object localization and the robustness of tracking, we propose Laplace-based Hash (LHash) and Laplace-based Difference Hash (LDHash). By qualitative and quantitative comparison with some representative tracking algorithms, experimental results show that our improved perceptual hashing-based tracking algorithms perform favorably against the state-of-the-art algorithms under various challenging environments in terms of time cost, accuracy and robustness. Since our improved perceptual hashing can be a compact and efficient representation of objects, it can be further applied to fusing with depth information for more robust RGB-D video tracking.

19 citations

01 Jan 2018
TL;DR: Machine learning methods can be used for solving important binary classification tasks in domains such as display advertising and recommender systems as mentioned in this paper, where categorical features have been used for classification tasks.
Abstract: Machine learning methods can be used for solving important binary classification tasks in domains such as display advertising and recommender systems. In many of these domains categorical features ...

19 citations

Proceedings ArticleDOI
26 Oct 1997
TL;DR: Theoretical analyses of the ForeSight method show that it is more than ten times as fast as a comparable point-based geometric hashing implementation, while using only one-quarter of the memory.
Abstract: We present a new method for the recognition of polyhedral objects from 2D images based on geometric hashing. Rather than the point-based approach of previous geometric hashing implementations, which tend to be rather sensitive to image noise and spurious data, our method is based on triples of connected edges. As well as improving the robustness of the system, the use of higher level feature groupings results in a very efficient specialisation of the geometric hashing paradigm. Theoretical analyses of the ForeSight method show that it is more than ten times as fast as a comparable point-based geometric hashing implementation, while using only one-quarter of the memory. These results were confirmed by practical experiments on a database of 50 real images, in which the recognition rate achieved by ForeSight approached twice that of the conventional method.

18 citations

Proceedings ArticleDOI
01 Jun 1992
TL;DR: A multi-directory hashing scheme, called fast search multi- directory hashing, and its generalization, called controlled searchmulti- Directory hashing, are presented and both methods achieve linearly increasing expected directory size with the number of records.
Abstract: The objective of this paper is to develop and analyze high performance hash based search methods for main memory databases. We define optimal search in main memory databases as the search that requires at most one key comparison to locate a record. Existing hashing techniques become impractical when they are adapted to yield optimal search in main memory databases because of their large directory size. Multi-directory hashing techniques can provide significantly improved directory utilization over single-directory hashing techniques. A multi-directory hashing scheme, called fast search multi-directory hashing, and its generalization, called controlled search multi-directory hashing, are presented. Both methods achieve linearly increasing expected directory size with the number of records. Their performance is compared to existing alternatives.

18 citations

Journal ArticleDOI
01 Nov 2014
TL;DR: The utility of genetic programming (GP) and avalanche effect to automatically generate noncryptographic hashes that can compete with state‐of‐the‐art hash functions is demonstrated.
Abstract: Noncryptographic hash functions have an immense number of important practical applications owing to their powerful search properties. However, those properties critically depend on good designs: Inappropriately chosen hash functions are a very common source of performance losses. On the other hand, hash functions are difficult to design: They are extremely nonlinear and counterintuitive, and relationships between the variables are often intricate and obscure. In this work, we demonstrate the utility of genetic programming GP and avalanche effect to automatically generate noncryptographic hashes that can compete with state-of-the-art hash functions. We describe the design and implementation of our system, called GP-hash, and its fitness function, based on avalanche properties. Also, we experimentally identify good terminal and function sets and parameters for this task, providing interesting information for future research in this topic. Using GP-hash, we were able to generate two different families of noncryptographic hashes. These hashes are able to compete with a selection of the most important functions of the hashing literature, most of them widely used in the industry and created by world-class hashing experts with years of experience.

18 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202289
202111
202016
201916
201838