Topic
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.
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TL;DR: Ajtai as mentioned in this paper described a construction of one-way functions whose security is equivalent to the difficulty of some well known approximation problems in lattices and showed that essentially the same construction can also be used to obtain collision-free hashing.
Abstract: In 1995, Ajtai described a construction of one-way functions whose security is equivalent to the difficulty of some well known approximation problems in lattices. We show that essentially the same construction can also be used to obtain collision-free hashing. This paper contains a self-contained proof sketch of Ajtai's result.
78 citations
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27 Jul 2015TL;DR: A data structure that reduces approximate nearest neighbor query times for image patches in large datasets by up to 9× over k-coherence, up to 12× over TreeCANN, and up to 200× over PatchMatch is presented.
Abstract: This paper presents a data structure that reduces approximate nearest neighbor query times for image patches in large datasets. Previous work in texture synthesis has demonstrated real-time synthesis from small exemplar textures. However, high performance has proved elusive for modern patch-based optimization techniques which frequently use many exemplar images in the tens of megapixels or above. Our new algorithm, PatchTable, offloads as much of the computation as possible to a pre-computation stage that takes modest time, so patch queries can be as efficient as possible. There are three key insights behind our algorithm: (1) a lookup table similar to locality sensitive hashing can be precomputed, and used to seed sufficiently good initial patch correspondences during querying, (2) missing entries in the table can be filled during pre-computation with our fast Voronoi transform, and (3) the initially seeded correspondences can be improved with a precomputed k-nearest neighbors mapping. We show experimentally that this accelerates the patch query operation by up to 9× over k-coherence, up to 12× over TreeCANN, and up to 200× over PatchMatch. Our fast algorithm allows us to explore efficient and practical imaging and computational photography applications. We show results for artistic video stylization, light field super-resolution, and multi-image editing.
77 citations
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11 Jul 2010TL;DR: This paper utilizes the norm-keeping property of p-stable functions to ensure that two data's collision probability reflects their non-metric distance in original feature space and investigates various concrete examples to validate the proposed algorithm.
Abstract: Non-metric distances are often more reasonable compared with metric ones in terms of consistency with human perceptions. However, existing locality-sensitive hashing (LSH) algorithms can only support data which are gauged with metrics. In this paper we propose a novel locality-sensitive hashing algorithm targeting such non-metric data. Data in original feature space are embedded into an implicit reproducing kernel Kreĭn space and then hashed to obtain binary bits. Here we utilize the norm-keeping property of p-stable functions to ensure that two data's collision probability reflects their non-metric distance in original feature space. We investigate various concrete examples to validate the proposed algorithm. Extensive empirical evaluations well illustrate its effectiveness in terms of accuracy and retrieval speedup.
76 citations
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TL;DR: Under the proposed framework, a hashing scheme using discrete cosine transform (DCT) and non-negative matrix factorization (NMF) is implemented, and experimental results show that the proposed scheme is resistant to normal content-preserving manipulations, and has a very low collision probability.
Abstract: Image hash is a content-based compact representation of an image for applications such as image copy detection, digital watermarking, and image authentication. This paper proposes a lexicographical-structured framework to generate image hashes. The system consists of two parts: dictionary construction and maintenance, and hash generation. The dictionary is a large collection of feature vectors called words, representing characteristics of various image blocks. It is composed of a number of sub-dictionaries, and each sub-dictionary contains many features, the number of which grows as the number of training images increase. The dictionary is used to provide basic building blocks, namely, the words, to form the hash. In the hash generation, blocks of the input image are represented by features associated to the sub-dictionaries. This is achieved by using a similarity metric to find the most similar feature among the selective features of each sub-dictionary. The corresponding features are combined to produce an intermediate hash. The final hash is obtained by encoding the intermediate hash. Under the proposed framework, we have implemented a hashing scheme using discrete cosine transform (DCT) and non-negative matrix factorization (NMF). Experimental results show that the proposed scheme is resistant to normal content-preserving manipulations, and has a very low collision probability.
76 citations
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TL;DR: A novel adaptive similarity measure which is consistent with k-nearest neighbor search is presented, and it is proved that it leads to a valid kernel if the original similarity function is a kernel function.
76 citations