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Locality-sensitive hashing

About: Locality-sensitive hashing is a research topic. Over the lifetime, 1894 publications have been published within this topic receiving 69362 citations.


Papers
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Proceedings ArticleDOI
06 Nov 2011
TL;DR: A novel technique called Comparison Hadamard random projection (CHRP) is proposed for further improving the efficiency of LSH within OMP and empirically validate CHRP's efficacy by performing a suite of experiments for image denoising, scene classification, and video categorization.
Abstract: Sparse projection has been shown to be highly effective in several domains, including image denoising and scene / object classification. However, practical application to large scale problems such as video analysis requires efficient versions of sparse projection algorithms such as Orthogonal Matching Pursuit (OMP). In particular, random projection based locality sensitive hashing (LSH) has been proposed for OMP. In this paper, we propose a novel technique called Comparison Hadamard random projection (CHRP) for further improving the efficiency of LSH within OMP. CHRP combines two techniques:(1) The Fast Johnson-Lindenstrauss Transform (FJLT) which uses a randomized Hadamard transform and sparse projection matrix for LSH, and (2) Achlioptas' random projection that uses only addition and comparison operations. Our approach provides the robustness of FJLT while completely avoiding multiplications. We empirically validate CHRP's efficacy by performing a suite of experiments for image denoising, scene classification, and video categorization. Our experiments indicate that CHRP significantly speeds-up OMP with negligible loss in classification accuracy.

24 citations

Journal ArticleDOI
TL;DR: An object-oriented feature selection mechanism for deep convolutional features from a pre-trained CNN that achieves better precision and recall than the full feature set for objects with varying backgrounds and reduces number of feature maps without performance degradation.

24 citations

01 Jan 2004
TL;DR: The finding is that the well-known function for hashing sequence of symbols, ELFhash, is not very good in this regard, and the other two functions are better and thus recommended.
Abstract: Hashing large collection of URLs is an inevitable problem in many Web research activities. Through a large scale experiment, three hash functions are compared in this paper. Two metrics were developed for the comparison, which are related to web structure analysis and Web crawling, respectively. The finding is that the well-known function for hashing sequence of symbols, ELFhash, is not very good in this regard, and the other two functions are better and thus recommended.

24 citations

Proceedings Article
19 Jun 2011
TL;DR: This paper proposes a novel (semi-)supervised hashing method named Semi-Supervised SimHash (S3H) for high-dimensional data similarity search that learns the optimal feature weights from prior knowledge to relocate the data such that similar data have similar hash codes.
Abstract: Searching documents that are similar to a query document is an important component in modern information retrieval. Some existing hashing methods can be used for efficient document similarity search. However, unsupervised hashing methods cannot incorporate prior knowledge for better hashing. Although some supervised hashing methods can derive effective hash functions from prior knowledge, they are either computationally expensive or poorly discriminative. This paper proposes a novel (semi-)supervised hashing method named Semi-Supervised SimHash (S3H) for high-dimensional data similarity search. The basic idea of S3H is to learn the optimal feature weights from prior knowledge to relocate the data such that similar data have similar hash codes. We evaluate our method with several state-of-the-art methods on two large datasets. All the results show that our method gets the best performance.

24 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202343
2022108
202188
2020110
2019104
2018139