Journal Article10.1016/J.PATREC.2007.08.007
Efficient high-dimensional indexing by sorting principal component
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TL;DR: This paper presents a new indexing structure based on vector approximation method, in which only a small part of approximation file need be accessed, and shows that the new approach provides a faster search speed than the other VA-file approaches.
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About: This article is published in Pattern Recognition Letters. The article was published on 01 Dec 2007. The article focuses on the topics: Search engine indexing & Search algorithm.
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Citations
Efficient nearest neighbor query based on extended B+-tree in high-dimensional space
TL;DR: This paper presents a new one-dimensional indexing scheme based on extended B^+-tree for k-nearest neighbor search in high-dimensional space, and presents a novel k-NEarest neighbors search algorithm which can guarantee the accuracy of query results.
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Exploiting lower bounds to accelerate approximate nearest neighbor search on high-dimensional data
Yingfan Liu,Hao Wei,Hong Cheng +2 more
TL;DR: The lower bounds are able to obviously accelerate ANN search of the existing indexing methods, and the lower bounds outperform the existing lower bounds by a significant margin, due to their strong pruning powers.
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Speed up Linear Scan in High-Dimensions Using Extended B+-Tree
Jiangtao Cui,Bin Xiao,Zhiliang Yin +2 more
- 06 Dec 2010
TL;DR: A new access idea implemented on linear scan based methods to speed up the nearest-neighbor queries is proposed, to map high-dimensional points into two kinds of one-dimensional values using projection and distance computation.
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A hybrid vector quantization combining a tree structure and a Voronoi diagram
TL;DR: A hybrid VQ combining a tree structure and a Voronoi diagram is proposed to improve VQ efficiency, and satisfies the requirements of handheld device application, namely, the use of limited memory and network bandwidth, when a suitable number of dimensions in principal component analysis is selected.
Effective optimizations of cluster-based nearest neighbor search in high-dimensional space
TL;DR: Experiments show the improvement of HB+ with respect to HB in terms of efficiency (I/O cost and CPU response time) and also demonstrate the superiority over other exact NN indexes.
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References
Texture features for browsing and retrieval of image data
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TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
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Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
Kaushik Chakrabarti,Sharad Mehrotra +1 more
- 10 Sep 2000
TL;DR: Local Dimensionality Reduction (LDR) is proposed that tries to find local correlations in the data and performs dimensionality reduction on the locally correlated clusters of data individually and an index structure is developed that exploits the correlated clusters to efficiently support point, range and k-nearest neighbor queries over high dimensional datasets.
Vector approximation based indexing for non-uniform high dimensional data sets
Hakan Ferhatosmanoglu,Ertem Tuncel,Divyakant Agrawal,Amr El Abbadi +3 more
- 06 Nov 2000
TL;DR: The VAle, a new technique for indexing high dimensional data sets based on vector approximations, is proposed and an evaluation of nearest neighbor queries shows that the VAle technique results in improvements over the current VAle approach for several real data sets.
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An efficient indexing method for nearest neighbor searches in high-dirnensional image databases
TL;DR: This paper introduces the local polar coordinate file (LPC-file), a filtering approach for nearest-neighbor searches in high-dimensional image databases that outperforms both of the VA-file and the sequential scan in total elapsed time and in the number of disk accesses and is robust in both "good" distributions and "bad" distributions.
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The GC-tree: a high-dimensional index structure for similarity search in image databases
Guang-Ho Cha,Chin-Wan Chung +1 more
TL;DR: The GC-tree is a new dynamic index structure based on a special subspace partitioning strategy which is optimized for a clustered high-dimensional image dataset and outperforms all other methods for efficient similarity search in image databases.
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