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DepthFirst KNearest Neighbor Finding Using the MaxNearestDist Estimator £
"... A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depthfirst branch and bound knearest neighbor finding algorithm. 1 ..."
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A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depthfirst branch and bound knearest neighbor finding algorithm. 1
To appear in PAMI KNearest Neighbor Finding Using MaxNearestDist
"... Similarity searching often reduces to finding the k nearest neighbors to a query object. Finding the k nearest neighbors is achieved by applying either a depthfirst or a bestfirst algorithm to the search hierarchy containing the data. These algorithms are generally applicable to any index based on ..."
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. Both the depthfirst and bestfirst knearest neighbor algorithms are modified to use MAXNEARESTDIST, which is shown to enhance both algorithms by overcoming their shortcomings. In particular, for the depthfirst algorithm, the number of clusters in the search hierarchy that must be examined
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 448 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these high
Optimal MultiStep kNearest Neighbor Search
, 1998
"... For an increasing number of modern database applications, efficient support of similarity search becomes an important task. Along with the complexity of the objects such as images, molecules and mechanical parts, also the complexity of the similarity models increases more and more. Whereas algorithm ..."
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Cited by 199 (23 self)
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, and our investigations substantiate that the number of candidates which are produced in the filter step and exactly evaluated in the refinement step is a fundamental efficiency parameter. After revealing the strong performance shortcomings of the stateoftheart algorithm for knearest neighbor search
Monitoring kNearest Neighbor Queries Over Moving Objects
"... Many locationbased applications require constant monitoring of knearest neighbor (kNN) queries over moving objects within a geographic area. Existing approaches to this problem have focused on predictive queries, and relied on the assumption that the trajectories of the objects are fully predicta ..."
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Cited by 127 (0 self)
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Many locationbased applications require constant monitoring of knearest neighbor (kNN) queries over moving objects within a geographic area. Existing approaches to this problem have focused on predictive queries, and relied on the assumption that the trajectories of the objects are fully
kNearest Neighbors in Uncertain Graphs
"... Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer knearest neig ..."
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Cited by 31 (4 self)
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Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer knearest
KNearest Neighbor Search for Fuzzy Objects
"... The KNearest Neighbor search (kNN) problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biom ..."
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Cited by 8 (1 self)
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The KNearest Neighbor search (kNN) problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas
Anytime KNearest Neighbor Search for Database Applications
 FIRST INTERNATIONAL WORKSHOP ON SIMILARITY SEARCH AND APPLICATIONS
, 2008
"... Many contemporary database applications require similaritybased retrieval of complex objects where the only usable knowledge of its domain is determined by a metric distance function. In support of these applications, we explored a search strategy for knearest neighbor searches with MVPtrees that ..."
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Cited by 2 (1 self)
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Many contemporary database applications require similaritybased retrieval of complex objects where the only usable knowledge of its domain is determined by a metric distance function. In support of these applications, we explored a search strategy for knearest neighbor searches with MVP
Shape Matching and Object Recognition Using Shape Contexts
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
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Cited by 1787 (21 self)
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for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning trans form. We treat recognition in a nearestneighbor classification framework as the problem of finding the stored
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