Similarity Search The Metric Space Approach

Zezula, Pavel; Amato, Giuseppe; Dohnal, Vlastislav; Batko, Michal

doi:10.1007/0-387-29151-2

Cited by 607 publications

(479 citation statements)

References 64 publications

(132 reference statements)

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“…In this way, metric spaces allow domain experts to model their notion of content-based similarity by an appropriate feature representation and distance function serving as similarity measure. At the same time, this approach allows database experts to design index structures, so-called metric access methods (or metric indexes) [8,13,29,31], for efficient query processing of content-based similarity queries in a database S ⊂ U. These methods rely on the distance function δ only, i.e., they do not necessarily know the structure of the feature representation of the objects.…”

Section: Metric Indexingmentioning

confidence: 99%

“…It has been shown that metric indexing [1] and ptolemaic indexing [19] reach a speed-up of more than two orders of magnitude with respect to the sequential scan. However, even when using indexing approaches, the speed-up is generally limited due to the high intrinsic dimensionality [31]. Thus, in order to use the SQFD for large-scale image retrieval, we propose to parallelize the SQFD query processing.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the quadratic complexity is still too high to use the SQFD for a sequential search of a large database. In order to reduce the computational effort, indexing [31] approaches have been applied to the SQFD. It has been shown that metric indexing [1] and ptolemaic indexing [19] reach a speed-up of more than two orders of magnitude with respect to the sequential scan.…”

Section: Introductionmentioning

confidence: 99%

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Combining CPU and GPU architectures for fast similarity search

Kruliš

Skopal

Lokoč

et al. 2012

Distrib Parallel Databases

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The Signature Quadratic Form Distance on feature signatures represents a flexible distance-based similarity model for effective content-based multimedia retrieval. Although metric indexing approaches are able to speed up query processing by two orders of magnitude, their applicability to large-scale multimedia databases containing billions of images is still a challenging issue. In this paper, we propose a parallel approach that balances the utilization of CPU and many-core GPUs for efficient similarity search with the Signature Quadratic Form Distance. In particular, we show how to process multiple distance computations and other parts of the search procedure in parallel, achieving maximal performance of the combined CPU/GPU system. The experimental evaluation demonstrates that our approach implemented on a common workstation with 2 GPU cards outperforms traditional parallel implementation on a high-end 48-core NUMA server in terms of efficiency almost by an order of magnitude. If we consider also the price of the high-end server that is ten times higher than that of the GPU workstation then, based on price/performance ratio, the GPU-based similarity search beats the CPU-based solution by almost two orders of magnitude. Although proposed for the SQFD, our approach of fast GPU-based simiCommunicated by: Kaushik Chakrabarti.M. Kruliš · T. Skopal ( ) · J. Lokoč

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Section: Metric Indexingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining CPU and GPU architectures for fast similarity search

Kruliš

Skopal

Lokoč

et al. 2012

Distrib Parallel Databases

View full text Add to dashboard Cite

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“…Han and Kamber 2006;Zezula et al 2006) As examples we describe two reusable components according to Tracz (1990) for measuring distances. Component Name: EUCLIDEAN…”

Section: Measure Distancementioning

confidence: 99%

Reusable components for partitioning clustering algorithms

et al. 2009

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Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific subproblems in a specific area. Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them.

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“…The superblock search is applicable to irregular points. Many other search strategies exist [25,27], and they are often designed for very large and high [23][24][25] are explored in this work. Additional study of other search structures could be undertaken.…”

Section: Searchmentioning

confidence: 99%

Implementation aspects of sequential Gaussian simulation on irregular points

Manchuk

Deutsch

2012

Comput Geosci

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The increasing use of unstructured grids for reservoir modeling motivates the development of geostatistical techniques to populate them with properties such as facies proportions, porosity and permeability. Unstructured grids are often populated by upscaling high-resolution regular grid models, but the size of the regular grid becomes unreasonably large to ensure that there is sufficient resolution for small unstructured grid elements. The properties could be modeled directly on the unstructured grid, which leads to an irregular configuration of points in the three-dimensional reservoir volume. Current implementations of Gaussian simulation for geostatistics are for regular grids. This paper addresses important implementation details involved in adapting sequential Gaussian simulation to populate irregular point configurations including general storage and computation issues, generating random paths for improved long range variogram reproduction, and search strategies including the superblock search and the k-dimensional tree. An efficient algorithm for computing the variogram of very large irregular point sets is developed for model checking.

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Similarity Search The Metric Space Approach

Cited by 607 publications

References 64 publications

Combining CPU and GPU architectures for fast similarity search

Combining CPU and GPU architectures for fast similarity search

Reusable components for partitioning clustering algorithms

Implementation aspects of sequential Gaussian simulation on irregular points

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