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Locality sensitive hashing

Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly, Yongwook Bryce Kim, Erik Hemberg, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly, Erik Hemberg
We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Yongwook Bryce Kim, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly, Yongwook Bryce Kim, Una-May O'Reilly
We apply the sublinear time, scalable locality-sensitive hashing (LSH) and majority discrimination to the problem of predicting critical events based on physiological waveform time series. Compared to using the linear exhaustive k-nearest neighbor search, our proposed method vastly speeds up prediction time up to 25 times while sacrificing only 1% of accuracy when demonstrated on an arterial blood pressure dataset extracted from the MIMIC2 database. We compare two widely used variants of LSH, the bit sampling based (L1LSH) and the random projection based (E2LSH) methods to measure their direct impact on retrieval and prediction accuracy...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Yunan Luo, Jianyang Zeng, Bonnie Berger, Jian Peng
No abstract text is available yet for this article.
April 2016: Research in Computational Molecular Biology: ... Annual International Conference, RECOMB ...: Proceedings
Mengyang Yu, Li Liu, Ling Shao
Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies. In this paper, we propose a novel unsupervised binary coding algorithm called binary set embedding (BSE) to obtain meaningful hash codes for local features from the image domain and words from text domain...
September 27, 2016: IEEE Transactions on Neural Networks and Learning Systems
Kun Ding, Chunlei Huo, Bin Fan, Shiming Xiang, Chunhong Pan
Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search...
October 24, 2016: IEEE Transactions on Neural Networks and Learning Systems
Ram Kumar Karsh, R H Laskar, Bhanu Bhai Richhariya
BACKGROUND: Image authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length...
2016: SpringerPlus
Luisa Verdoliva, Davide Cozzolino, Giovanni Poggi
We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors. Data vectors are classified based on the index and sign of their largest components, thereby partitioning the space in a number of cones centered in the origin. The query is itself classified, and the search starts from the selected cone and proceeds to neighboring ones. Overall, the proposed algorithm corresponds to locality sensitive hashing in the space of directions, with hashing based on the order of components...
January 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yibing Ma, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yushan Zheng, Huaqiang Shi, Yu Zhao
In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate and fast retrieval method for breast histopathological image. Specifically, the method presents local statistical feature of nuclei for morphology and distribution of nuclei, and employs Gabor feature to describe texture information...
September 20, 2016: IEEE Journal of Biomedical and Health Informatics
Yongwook Bryce Kim, Una-May O'Reilly
We propose a fast, scalable locality-sensitive hashing method for the problem of retrieving similar physiological waveform time series. When compared to the naive k-nearest neighbor search, the method vastly speeds up the retrieval time of similar physiological waveforms without sacrificing significant accuracy. Our result shows that we can achieve 95% retrieval accuracy or better with up to an order of magnitude of speed-up. The extra time required in advance to create the optimal data structure is recovered when query quantity equals 15% of the repository, while the method incurs a trivial additional memory cost...
August 2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Youji Feng, Lixin Fan, Yihong Wu
The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast...
January 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Konstantin Berlin, Sergey Koren, Chen-Shan Chin, James P Drake, Jane M Landolin, Adam M Phillippy
No abstract text is available yet for this article.
October 2015: Nature Biotechnology
Xianglong Liu, Bowen Du, Cheng Deng, Ming Liu, Bo Lang
Hashing has been proved as an attractive solution to approximate nearest neighbor search, owing to its theoretical guarantee and computational efficiency. Though most of prior hashing algorithms can achieve low memory and computation consumption by pursuing compact hash codes, however, they are still far beyond the capability of learning discriminative hash functions from the data with complex inherent structure among them. To address this issue, in this paper, we propose a structure sensitive hashing based on cluster prototypes, which explicitly exploits both global and local structures...
October 2016: IEEE Transactions on Cybernetics
Simon Korman, Shai Avidan
Coherency Sensitive Hashing (CSH) extends Locality Sensitivity Hashing (LSH) and PatchMatch to quickly find matching patches between two images. LSH relies on hashing, which maps similar patches to the same bin, in order to find matching patches. PatchMatch, on the other hand, relies on the observation that images are coherent, to propagate good matches to their neighbors in the image plane, using random patch assignment to seed the initial matching. CSH relies on hashing to seed the initial patch matching and on image coherence to propagate good matches...
June 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Relja Arandjelović, Andrew Zisserman
The goal of this work is a data structure to support approximate nearest neighbor search on very large scale sets of vector descriptors. The criteria we wish to optimize are: (i) that the memory footprint of the representation should be very small (so that it fits into main memory); and (ii) that the approximation of the original vectors should be accurate. We introduce a novel encoding method, named a Set Compression Tree (SCT), that satisfies these criteria. It is able to accurately compress 1 million descriptors using only a few bits per descriptor...
December 2014: IEEE Transactions on Pattern Analysis and Machine Intelligence
Jianqiu Ji, Shuicheng Yan, Jianmin Li, Guangyu Gao, Qi Tian, Bo Zhang
Sign-random-projection locality-sensitive hashing (SRP-LSH) is a widely used hashing method, which provides an unbiased estimate of pairwise angular similarity, yet may suffer from its large estimation variance. We propose in this work batch-orthogonal locality-sensitive hashing (BOLSH), as a significant improvement of SRP-LSH. Instead of independent random projections, BOLSH makes use of batch-orthogonalized random projections, i.e, we divide random projection vectors into several batches and orthogonalize the vectors in each batch respectively...
October 2014: IEEE Transactions on Pattern Analysis and Machine Intelligence
Heng Zhang, Yanli Liu, Jindong Tan
A kind of multi feature points matching algorithm fusing local geometric constraints is proposed for the purpose of quickly loop closing detection in RGB-D Simultaneous Localization and Mapping (SLAM). The visual feature is encoded with BRAND (binary robust appearance and normals descriptor), which efficiently combines appearance and geometric shape information from RGB-D images. Furthermore, the feature descriptors are stored using the Locality-Sensitive-Hashing (LSH) technique and hierarchical clustering trees are used to search for these binary features...
2015: Sensors
Jingjing Wang, Chen Lin
Locality Sensitive Hashing (LSH) has been proposed as an efficient technique for similarity joins for high dimensional data. The efficiency and approximation rate of LSH depend on the number of generated false positive instances and false negative instances. In many domains, reducing the number of false positives is crucial. Furthermore, in some application scenarios, balancing false positives and false negatives is favored. To address these problems, in this paper we propose Personalized Locality Sensitive Hashing (PLSH), where a new banding scheme is embedded to tailor the number of false positives, false negatives, and the sum of both...
2015: Computational Intelligence and Neuroscience
Kurt Schmidlin, Kerri M Clough-Gorr, Adrian Spoerri
BACKGROUND: Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information...
2015: BMC Medical Research Methodology
Konstantin Berlin, Sergey Koren, Chen-Shan Chin, James P Drake, Jane M Landolin, Adam M Phillippy
Long-read, single-molecule real-time (SMRT) sequencing is routinely used to finish microbial genomes, but available assembly methods have not scaled well to larger genomes. We introduce the MinHash Alignment Process (MHAP) for overlapping noisy, long reads using probabilistic, locality-sensitive hashing. Integrating MHAP with the Celera Assembler enabled reference-grade de novo assemblies of Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster and a human hydatidiform mole cell line (CHM1) from SMRT sequencing...
June 2015: Nature Biotechnology
Jun-yi Li, Jian-hua Li
Fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing, which guarantee our approach's linear time; however existing methods still do not solve the problem of locality-sensitive hashing (LSH) algorithm and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing...
2015: TheScientificWorldJournal
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