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344
Similarity Indexing: Algorithms and Performance
 In Proceedings SPIE Storage and Retrieval for Image and Video Databases
, 1996
"... Efficient indexing support is essential to allow contentbased image and video databases using similaritybased retrieval to scale to large databases (tens of thousands up to millions of images). In this paper, we take an in depth look at this problem. One of the major difficulties in solving this pr ..."
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Cited by 127 (1 self)
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Efficient indexing support is essential to allow contentbased image and video databases using similaritybased retrieval to scale to large databases (tens of thousands up to millions of images). In this paper, we take an in depth look at this problem. One of the major difficulties in solving this problem is the high dimension (6100) of the feature vectors that are used to represent objects. We provide an overview of the work in computational geometry on this problem and highlight the results we found are most useful in practice, including the use of approximate nearest neighbor algorithms. We also present a variant of the optimized kd tree we call the VAM kd tree, and provide algorithms to create an optimized Rtree we call the VAMSplit Rtree. We found that the VAMSplit Rtree provided better overall performance than all competing structures we tested for main memory and secondary memory applications. We observed large improvements in performance relative to the R*tree and SStree in secondary memory applications, and modest improvements relative to optimized kd tree variants.Nearest Neighbor Search
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
, 2001
"... With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighb ..."
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Cited by 120 (9 self)
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With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While algorithms have been proposed that compute RNN queries for nonmoving objects, there have been no proposals for answering RNN queries for continuously moving objects. Another such query is the nearest neighbor (NN) query, which has been studied extensively and in many contexts. Like the RNN query, the NN query has not been explored for moving query and data points. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. As a part of the solution to this problem and as a separate contribution, an algorithm for answering NN queries for continuously moving points is also proposed. The results of performance experiments are reported.
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
 In Proceedings of ICDE’99
, 1999
"... Feature based similarity search is emerging as an important search paradigm in database systems. The technique used is to map the data items as points into a high dimensional feature space which is indexed using a multidimensional data structure. Similarity search then corresponds to a range search ..."
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Cited by 117 (13 self)
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Feature based similarity search is emerging as an important search paradigm in database systems. The technique used is to map the data items as points into a high dimensional feature space which is indexed using a multidimensional data structure. Similarity search then corresponds to a range search over the data structure. Although several data structures have been proposed for feature indexing, none of them is known to scale beyond 1015 dimensional spaces. This paper introduces the hybrid tree – a multidimensional data structure for indexing high dimensional feature spaces. Unlike other multidimensional data structures, the hybrid tree cannot be classified as either a pure data partitioning (DP) index structure (e.g., Rtree, SStree, SRtree) or a pure space partitioning (SP) one (e.g., KDBtree, hBtree); rather, it “combines ” positive aspects of the two types of index structures a single data structure to achieve search performance more scalable to high dimensionalities than either of the above techniques (hence, the name “hybrid”). Furthermore, unlike many data structures (e.g., distance based index structures like SStree, SRtree), the hybrid tree can support queries based on arbitrary distance functions. Our experiments on “real” high dimensional large size feature databases demonstrate that the hybrid tree scales well to high dimensionality and large database sizes. It significantly outperforms both purely DPbased and SPbased index mechanisms as well as linear scan at all dimensionalities for large sized databases. 1.
Dimensionality Reduction for Similarity Searching in Dynamic Databases
, 1998
"... Databases are increasingly being used to store multimedia objects such as maps, images, audio and video. Storage and retrieval of these objects is accomplished using multidimensional index structures such as R*trees and SStrees. As dimensionality increases, query performance in these index struc ..."
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Cited by 112 (6 self)
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Databases are increasingly being used to store multimedia objects such as maps, images, audio and video. Storage and retrieval of these objects is accomplished using multidimensional index structures such as R*trees and SStrees. As dimensionality increases, query performance in these index structures degrades. This phenomenon, generally referred to as the dimensionality curse, can be circumvented by reducing the dimensionality of the data. Such a reduction is however accompanied by a loss of precision of query results. Current techniques such as QBIC use SVD transformbased dimensionality reduction to ensure high query precision. The drawback of this approach is that SVD is expensive to compute, and therefore not readily applicable to dynamic databases. In this paper, we propose novel techniques for performing SVDbased dimensionality reduction in dynamic databases. When the data distribution changes considerably so as to degrade query precision, we recompute the SVD transform a...
The Atree: An Index Structure for HighDimensional Spaces Using Relative Approximation
, 2000
"... We propose a novel index structure, Atree (Approximation tree), for similarity search of highdimensional data. The basic idea of the Atree is the introduction of Virtual Bounding Rectangles (VBRs), which contain and approximate MBRs and data objects. VBRs can be represented rather compactly, and ..."
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Cited by 107 (0 self)
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We propose a novel index structure, Atree (Approximation tree), for similarity search of highdimensional data. The basic idea of the Atree is the introduction of Virtual Bounding Rectangles (VBRs), which contain and approximate MBRs and data objects. VBRs can be represented rather compactly, and thus affect the tree configuration both quantitatively and qualitatively. Firstly, since tree nodes can install large number of entries of VBRs, fanout of nodes becomes large, thus leads to fast search. More importantly, we have a free hand in arranging MBRs and VBRs in tree nodes. In the Atrees, nodes contain entries of an MBR and its children VBRs. Therefore, by fetching a node of an Atree, we can obtain the information of exact position of a parent MBR and approximate position of its children. We have performed experiments using both synthetic and real data sets. For the real data sets, the Atree outperforms the SRtree and the VAFile in all range of dimensionality up to 64 dimension, which is the highest dimension in our experiments. The Atree achieves 77.3 % (77.7%, resp.) savings in page accesses compared to the SRtree (the VAFile, resp.) for 64dimensional real data.
Independent Quantization: An Index Compression Technique for HighDimensional Data Spaces
 IN ICDE
, 1999
"... Two major approaches have been proposed to efficiently process queries in databases: Speeding up the search by using index structures, and speeding up the search by operating on a compressed database, such as a signature file. Both approaches have their limitations: Indexing techniques are inefficie ..."
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Cited by 91 (22 self)
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Two major approaches have been proposed to efficiently process queries in databases: Speeding up the search by using index structures, and speeding up the search by operating on a compressed database, such as a signature file. Both approaches have their limitations: Indexing techniques are inefficient in extreme configurations, such as highdimensional spaces, where even a simple scan may be cheaper than an indexbased search. Compression techniques are not very efficient in all other situations. We propose to combine both techniques to search for nearest neighbors in a highdimensional space. For this purpose, we develop a compressed index, called the IQtree, with a threelevel structure: The first level is a regular (flat) directory consisting of minimum bounding boxes, the second level contains data points in a compressed representation, and the third level contains the actual data. We overcome several engineering challenges in constructing an effective index structure of this type...
iDistance: An Adaptive B+tree Based Indexing Method for Nearest Neighbor Search
"... In this paper, we present an efficient B+tree based indexing method, called iDistance, for Knearest neighbor (KNN) search in a highdimensional metric space. iDistance partitions the data based on a space or datapartitioning strategy, and selects a reference point for each partition. The data po ..."
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Cited by 90 (10 self)
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In this paper, we present an efficient B+tree based indexing method, called iDistance, for Knearest neighbor (KNN) search in a highdimensional metric space. iDistance partitions the data based on a space or datapartitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a B +tree structure and KNN search to be performed using onedimensional range search. The choice of partition and reference point adapt the index structure to the data distribution. We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization.
Supporting Ranked Boolean Similarity Queries in MARS
, 1998
"... To address the emerging needs of applications that require access to and retrieval of multimedia objects, we are developing the Multimedia Analysis and Retrieval System (MARS) [29]. In this paper, we concentrate on the retrieval subsystem of MARS and its support for contentbased queries over image ..."
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Cited by 88 (13 self)
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To address the emerging needs of applications that require access to and retrieval of multimedia objects, we are developing the Multimedia Analysis and Retrieval System (MARS) [29]. In this paper, we concentrate on the retrieval subsystem of MARS and its support for contentbased queries over image databases. Contentbased retrieval techniques have been extensively studied for textual documents in the area of automatic information retrieval [40, 4]. This paper describes how these techniques can be adapted for ranked retrieval over image databases. Specifically, we discuss the ranking and retrieval algorithms developed in MARS based on the Boolean retrieval model and describe the results of our experiments that demonstrate the effectiveness of the developed model for image retrieval.
Indexing the Distance: An Efficient Method to KNN Processing
, 2001
"... In this paper, we present an efficient method, called iDistance, for Knearest neighbor (KNN) search in a highdimensional space. iDistance partitions the data and selects a reference point for each partition. The data in each cluster are transformed into a single dimensional space based on their si ..."
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Cited by 82 (16 self)
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In this paper, we present an efficient method, called iDistance, for Knearest neighbor (KNN) search in a highdimensional space. iDistance partitions the data and selects a reference point for each partition. The data in each cluster are transformed into a single dimensional space based on their similarity with respect to a reference point. This allows the points to be indexed using a B + tree structure and KNN search be performed using onedimensional range search. The choice of partition and reference point provides the iDistance technique with degrees of freedom most other techniques do not have. We describe how appropriate choices here can effectively adapt the index structure to the data distribution. We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness.
Similarity search over time series data using wavelets
 In ICDE
, 2002
"... We consider the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity search over highdimensional timeseries data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this applicatio ..."
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Cited by 81 (0 self)
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We consider the use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity search over highdimensional timeseries data. While numerous transformations have been proposed and studied, the only wavelet that has been shown to be effective for this application is the Haar wavelet. In this work, we observe that a large class of wavelet transformations (not only orthonormal wavelets but also biorthonormal wavelets)can be used to support similarity search. This class includes the most popular and most effective wavelets being used in image compression. We present a detailed performance study of the effects of using different wavelets on the performance of similarity search for timeseries data. We include several wavelets that outperform both the Haar wavelet and the best known nonwavelet transformations for this application. To ensure our results are usable by an application engineer, we also show how to configure an indexing strategy for the best performing transformations. Finally, we identify classes of data that can be indexed efficiently using these wavelet transformations. 1.