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Locality sensitive hashing

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 1, 2015: 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
Xiaofan Zhang, Lin Yang, Wei Liu, Hai Su, Shaoting Zhang
With a continuous growing amount of annotated histopathological images, large-scale and data-driven methods potentially provide the promise of bridging the semantic gap between these images and their diagnoses. The purpose of this paper is to increase the scale at which automated systems can entail scalable analysis of histopathological images in massive databases. Specifically, we propose a principled framework to unify hashing-based image retrieval and supervised learning. Concretely, composite hashing is designed to simultaneously fuse and compress multiple high-dimensional image features into tens of binary hash bits, enabling scalable image retrieval with a very low computational cost...
2014: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jianke Zhu, Chenxia Wu, Chun Chen, Deng Cai
Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches, we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. To effectively extract the binary features from each patch surrounding the keypoint, we make use of treelets transform that can group the highly correlated data together and reduce the noise through the local analysis. Treelets is a multiresolution analysis tool, which provides an orthogonal basis to reflect the geometry of the noise-free data...
October 2015: IEEE Transactions on Cybernetics
Jingkuan Song, Yi Yang, Xuelong Li, Zi Huang, Yang Yang
Similarity search plays an important role in many applications involving high-dimensional data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the data...
July 2014: IEEE Transactions on Cybernetics
Zhenyu Wu, Ming Zou
An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data...
October 2014: Neural Networks: the Official Journal of the International Neural Network Society
M Chironna, S Tafuri, M S Gallone, A Sallustio, D Martinelli, R Prato, C Germinario
OBJECTIVES: To describe an outbreak of acute gastroenteritis in people who had eaten at a hash house in southern Italy. STUDY DESIGN: Case-control study. METHODS: A clinical case of gastroenteritis was defined as a person who had eaten at the hash house from 29 August to 4 September 2011 and who experienced defined gastrointestinal symptoms within 72 hours, or a person with a laboratory-confirmed salmonella infection without symptoms. A convenience sample was enrolled as the control group...
May 2014: Public Health
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